diff --git "a/metadata_train.csv" "b/metadata_train.csv"
new file mode 100644--- /dev/null
+++ "b/metadata_train.csv"
@@ -0,0 +1,6366 @@
+Chart,Question,Id
+diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",11103
+vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",7483
+Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",12686
+Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",2534
+adult_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",1617
+Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",12687
+Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",12688
+BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",10124
+diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",11100
+adult_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",1614
+Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",9625
+heart_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",4006
+BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",10125
+Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",9623
+diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",11101
+Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",2535
+Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",2536
+BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",10126
+adult_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",1616
+Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",7995
+Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",7998
+vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",7485
+Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",7996
+diabetes_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",11102
+heart_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",4005
+Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",9622
+Iris_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",7997
+Churn_Modelling_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",2537
+Breast_Cancer_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",12685
+Wine_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",9624
+vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are numeric, we might be facing the curse of dimensionality.",7482
+heart_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",4008
+BankNoteAuthentication_nr_records_nr_variables.png,"Given the number of records and that some variables are symbolic, we might be facing the curse of dimensionality.",10127
+heart_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",4007
+vehicle_nr_records_nr_variables.png,"Given the number of records and that some variables are date, we might be facing the curse of dimensionality.",7484
+adult_nr_records_nr_variables.png,"Given the number of records and that some variables are binary, we might be facing the curse of dimensionality.",1615
+Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 60.,12682
+adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 54.,1610
+vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 92.,7478
+Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 44.,7991
+heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 74.,3999
+Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 22.,2531
+vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 45.,7477
+Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 23.,12680
+Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 89.,2529
+Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 43.,2532
+Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 29.,7993
+Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 86.,2528
+BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 22.,10121
+Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 95.,12683
+diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 84.,11097
+adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 32.,1612
+vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 25.,7476
+heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 16.,4001
+Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 68.,12679
+Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 29.,9620
+adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 89.,1608
+heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 97.,4000
+diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 26.,11095
+BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 71.,10122
+Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 100.,7989
+Breast_Cancer_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 67.,12681
+BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 19.,10120
+Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 99.,9618
+diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 72.,11094
+Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 21.,7992
+Churn_Modelling_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 98.,2530
+diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 38.,11096
+BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 62.,10118
+vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 59.,7479
+Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 90.,9619
+Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 95.,9617
+Wine_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 71.,9616
+heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 47.,4003
+BankNoteAuthentication_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 51.,10119
+diabetes_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 75.,11098
+heart_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 49.,4002
+adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 90.,1609
+vehicle_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 84.,7480
+Iris_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 91.,7990
+adult_correlation_heatmap.png,The intrinsic dimensionality of this dataset is 16.,1611
+Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as binary.",2525
+Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as symbolic.",12678
+BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as binary.",10115
+vehicle_histograms.png,"All variables, but the class, should be dealt with as numeric.",7472
+Wine_histograms.png,"All variables, but the class, should be dealt with as numeric.",9612
+diabetes_histograms.png,"All variables, but the class, should be dealt with as date.",11092
+Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as numeric.",2524
+Wine_histograms.png,"All variables, but the class, should be dealt with as date.",9614
+heart_histograms.png,"All variables, but the class, should be dealt with as symbolic.",3998
+Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as binary.",12676
+heart_histograms.png,"All variables, but the class, should be dealt with as binary.",3996
+Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as numeric.",12675
+vehicle_histograms.png,"All variables, but the class, should be dealt with as date.",7474
+heart_histograms.png,"All variables, but the class, should be dealt with as numeric.",3995
+Iris_histograms.png,"All variables, but the class, should be dealt with as date.",7987
+adult_histograms.png,"All variables, but the class, should be dealt with as date.",1606
+BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as symbolic.",10117
+adult_histograms.png,"All variables, but the class, should be dealt with as binary.",1605
+heart_histograms.png,"All variables, but the class, should be dealt with as date.",3997
+Iris_histograms.png,"All variables, but the class, should be dealt with as symbolic.",7988
+BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as date.",10116
+Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as symbolic.",2527
+Churn_Modelling_histograms.png,"All variables, but the class, should be dealt with as date.",2526
+adult_histograms.png,"All variables, but the class, should be dealt with as symbolic.",1607
+BankNoteAuthentication_histograms.png,"All variables, but the class, should be dealt with as numeric.",10114
+adult_histograms.png,"All variables, but the class, should be dealt with as numeric.",1604
+vehicle_histograms.png,"All variables, but the class, should be dealt with as symbolic.",7475
+Wine_histograms.png,"All variables, but the class, should be dealt with as symbolic.",9615
+vehicle_histograms.png,"All variables, but the class, should be dealt with as binary.",7473
+Wine_histograms.png,"All variables, but the class, should be dealt with as binary.",9613
+Iris_histograms.png,"All variables, but the class, should be dealt with as binary.",7986
+Iris_histograms.png,"All variables, but the class, should be dealt with as numeric.",7985
+Breast_Cancer_histograms.png,"All variables, but the class, should be dealt with as date.",12677
+diabetes_histograms.png,"All variables, but the class, should be dealt with as symbolic.",11093
+diabetes_histograms.png,"All variables, but the class, should be dealt with as numeric.",11090
+diabetes_histograms.png,"All variables, but the class, should be dealt with as binary.",11091
+Churn_Modelling_histograms.png,The variable HasCrCard can be seen as ordinal.,2521
+Breast_Cancer_histograms.png,The variable perimeter_se can be seen as ordinal.,12668
+heart_histograms.png,The variable sex can be seen as ordinal.,3983
+vehicle_histograms.png,The variable RADIUS RATIO can be seen as ordinal.,7457
+heart_histograms.png,The variable exang can be seen as ordinal.,3990
+Churn_Modelling_histograms.png,The variable EstimatedSalary can be seen as ordinal.,2523
+BankNoteAuthentication_histograms.png,The variable curtosis can be seen as ordinal.,10112
+Wine_histograms.png,The variable Alcalinity of ash can be seen as ordinal.,9604
+adult_histograms.png,The variable education can be seen as ordinal.,1594
+Churn_Modelling_histograms.png,The variable Gender can be seen as ordinal.,2516
+vehicle_histograms.png,The variable MAJORSKEWNESS can be seen as ordinal.,7467
+adult_histograms.png,The variable hours-per-week can be seen as ordinal.,1603
+diabetes_histograms.png,The variable SkinThickness can be seen as ordinal.,11085
+vehicle_histograms.png,The variable PR AXIS ASPECT RATIO can be seen as ordinal.,7458
+BankNoteAuthentication_histograms.png,The variable entropy can be seen as ordinal.,10113
+adult_histograms.png,The variable educational-num can be seen as ordinal.,1595
+vehicle_histograms.png,The variable HOLLOWS RATIO can be seen as ordinal.,7471
+vehicle_histograms.png,The variable MINORKURTOSIS can be seen as ordinal.,7469
+Wine_histograms.png,The variable OD280-OD315 of diluted wines can be seen as ordinal.,9611
+Breast_Cancer_histograms.png,The variable symmetry_se can be seen as ordinal.,12671
+heart_histograms.png,The variable thal can be seen as ordinal.,3994
+Breast_Cancer_histograms.png,The variable perimeter_worst can be seen as ordinal.,12674
+BankNoteAuthentication_histograms.png,The variable skewness can be seen as ordinal.,10111
+adult_histograms.png,The variable capital-loss can be seen as ordinal.,1602
+diabetes_histograms.png,The variable Glucose can be seen as ordinal.,11083
+diabetes_histograms.png,The variable BloodPressure can be seen as ordinal.,11084
+Wine_histograms.png,The variable Hue can be seen as ordinal.,9610
+Wine_histograms.png,The variable Alcohol can be seen as ordinal.,9601
+heart_histograms.png,The variable fbs can be seen as ordinal.,3987
+vehicle_histograms.png,The variable DISTANCE CIRCULARITY can be seen as ordinal.,7456
+diabetes_histograms.png,The variable Insulin can be seen as ordinal.,11086
+heart_histograms.png,The variable cp can be seen as ordinal.,3984
+BankNoteAuthentication_histograms.png,The variable variance can be seen as ordinal.,10110
+vehicle_histograms.png,The variable MINORSKEWNESS can be seen as ordinal.,7468
+Churn_Modelling_histograms.png,The variable IsActiveMember can be seen as ordinal.,2522
+Breast_Cancer_histograms.png,The variable radius_worst can be seen as ordinal.,12672
+Iris_histograms.png,The variable SepalLengthCm can be seen as ordinal.,7981
+Churn_Modelling_histograms.png,The variable CreditScore can be seen as ordinal.,2514
+adult_histograms.png,The variable fnlwgt can be seen as ordinal.,1593
+heart_histograms.png,The variable thalach can be seen as ordinal.,3989
+vehicle_histograms.png,The variable CIRCULARITY can be seen as ordinal.,7455
+Churn_Modelling_histograms.png,The variable Geography can be seen as ordinal.,2515
+Breast_Cancer_histograms.png,The variable texture_worst can be seen as ordinal.,12673
+diabetes_histograms.png,The variable BMI can be seen as ordinal.,11087
+adult_histograms.png,The variable gender can be seen as ordinal.,1600
+Churn_Modelling_histograms.png,The variable NumOfProducts can be seen as ordinal.,2520
+heart_histograms.png,The variable chol can be seen as ordinal.,3986
+adult_histograms.png,The variable age can be seen as ordinal.,1591
+vehicle_histograms.png,The variable MAX LENGTH ASPECT RATIO can be seen as ordinal.,7459
+vehicle_histograms.png,The variable COMPACTNESS can be seen as ordinal.,7454
+Churn_Modelling_histograms.png,The variable Balance can be seen as ordinal.,2519
+adult_histograms.png,The variable capital-gain can be seen as ordinal.,1601
+Wine_histograms.png,The variable Flavanoids can be seen as ordinal.,9606
+vehicle_histograms.png,The variable MAJORVARIANCE can be seen as ordinal.,7464
+Churn_Modelling_histograms.png,The variable Tenure can be seen as ordinal.,2518
+heart_histograms.png,The variable trestbps can be seen as ordinal.,3985
+diabetes_histograms.png,The variable Age can be seen as ordinal.,11089
+adult_histograms.png,The variable race can be seen as ordinal.,1599
+heart_histograms.png,The variable age can be seen as ordinal.,3982
+Wine_histograms.png,The variable Ash can be seen as ordinal.,9603
+vehicle_histograms.png,The variable LENGTHRECTANGULAR can be seen as ordinal.,7463
+Churn_Modelling_histograms.png,The variable Age can be seen as ordinal.,2517
+adult_histograms.png,The variable relationship can be seen as ordinal.,1598
+diabetes_histograms.png,The variable Pregnancies can be seen as ordinal.,11082
+vehicle_histograms.png,The variable ELONGATEDNESS can be seen as ordinal.,7461
+Wine_histograms.png,The variable Malic acid can be seen as ordinal.,9602
+Wine_histograms.png,The variable Color intensity can be seen as ordinal.,9609
+Breast_Cancer_histograms.png,The variable area_se can be seen as ordinal.,12669
+Breast_Cancer_histograms.png,The variable smoothness_se can be seen as ordinal.,12670
+heart_histograms.png,The variable ca can be seen as ordinal.,3993
+diabetes_histograms.png,The variable DiabetesPedigreeFunction can be seen as ordinal.,11088
+vehicle_histograms.png,The variable MAJORKURTOSIS can be seen as ordinal.,7470
+Iris_histograms.png,The variable PetalWidthCm can be seen as ordinal.,7984
+Iris_histograms.png,The variable PetalLengthCm can be seen as ordinal.,7983
+Wine_histograms.png,The variable Nonflavanoid phenols can be seen as ordinal.,9607
+heart_histograms.png,The variable restecg can be seen as ordinal.,3988
+heart_histograms.png,The variable slope can be seen as ordinal.,3992
+vehicle_histograms.png,The variable MINORVARIANCE can be seen as ordinal.,7465
+Breast_Cancer_histograms.png,The variable texture_mean can be seen as ordinal.,12665
+Wine_histograms.png,The variable Total phenols can be seen as ordinal.,9605
+adult_histograms.png,The variable marital-status can be seen as ordinal.,1596
+adult_histograms.png,The variable workclass can be seen as ordinal.,1592
+Wine_histograms.png,The variable Proanthocyanins can be seen as ordinal.,9608
+Iris_histograms.png,The variable SepalWidthCm can be seen as ordinal.,7982
+vehicle_histograms.png,The variable GYRATIONRADIUS can be seen as ordinal.,7466
+Breast_Cancer_histograms.png,The variable texture_se can be seen as ordinal.,12667
+heart_histograms.png,The variable oldpeak can be seen as ordinal.,3991
+Breast_Cancer_histograms.png,The variable perimeter_mean can be seen as ordinal.,12666
+vehicle_histograms.png,The variable SCATTER RATIO can be seen as ordinal.,7460
+adult_histograms.png,The variable occupation can be seen as ordinal.,1597
+vehicle_histograms.png,The variable PR AXISRECTANGULAR can be seen as ordinal.,7462
+adult_histograms.png,The variable fnlwgt can be seen as ordinal without losing information.,1580
+Churn_Modelling_histograms.png,The variable EstimatedSalary can be seen as ordinal without losing information.,2513
+diabetes_histograms.png,The variable BMI can be seen as ordinal without losing information.,11079
+diabetes_histograms.png,The variable BloodPressure can be seen as ordinal without losing information.,11076
+heart_histograms.png,The variable ca can be seen as ordinal without losing information.,3980
+heart_histograms.png,The variable restecg can be seen as ordinal without losing information.,3975
+Churn_Modelling_histograms.png,The variable Tenure can be seen as ordinal without losing information.,2508
+BankNoteAuthentication_histograms.png,The variable curtosis can be seen as ordinal without losing information.,10108
+vehicle_histograms.png,The variable PR AXISRECTANGULAR can be seen as ordinal without losing information.,7444
+vehicle_histograms.png,The variable COMPACTNESS can be seen as ordinal without losing information.,7436
+diabetes_histograms.png,The variable SkinThickness can be seen as ordinal without losing information.,11077
+Breast_Cancer_histograms.png,The variable texture_se can be seen as ordinal without losing information.,12657
+diabetes_histograms.png,The variable DiabetesPedigreeFunction can be seen as ordinal without losing information.,11080
+BankNoteAuthentication_histograms.png,The variable variance can be seen as ordinal without losing information.,10106
+heart_histograms.png,The variable thalach can be seen as ordinal without losing information.,3976
+Wine_histograms.png,The variable Hue can be seen as ordinal without losing information.,9599
+adult_histograms.png,The variable educational-num can be seen as ordinal without losing information.,1582
+Churn_Modelling_histograms.png,The variable NumOfProducts can be seen as ordinal without losing information.,2510
+Wine_histograms.png,The variable OD280-OD315 of diluted wines can be seen as ordinal without losing information.,9600
+Wine_histograms.png,The variable Alcohol can be seen as ordinal without losing information.,9590
+heart_histograms.png,The variable sex can be seen as ordinal without losing information.,3970
+Wine_histograms.png,The variable Ash can be seen as ordinal without losing information.,9592
+vehicle_histograms.png,The variable PR AXIS ASPECT RATIO can be seen as ordinal without losing information.,7440
+vehicle_histograms.png,The variable MAJORSKEWNESS can be seen as ordinal without losing information.,7449
+adult_histograms.png,The variable race can be seen as ordinal without losing information.,1586
+vehicle_histograms.png,The variable MAJORKURTOSIS can be seen as ordinal without losing information.,7452
+vehicle_histograms.png,The variable MAJORVARIANCE can be seen as ordinal without losing information.,7446
+heart_histograms.png,The variable oldpeak can be seen as ordinal without losing information.,3978
+adult_histograms.png,The variable marital-status can be seen as ordinal without losing information.,1583
+Wine_histograms.png,The variable Color intensity can be seen as ordinal without losing information.,9598
+diabetes_histograms.png,The variable Glucose can be seen as ordinal without losing information.,11075
+Churn_Modelling_histograms.png,The variable Geography can be seen as ordinal without losing information.,2505
+Breast_Cancer_histograms.png,The variable symmetry_se can be seen as ordinal without losing information.,12661
+heart_histograms.png,The variable chol can be seen as ordinal without losing information.,3973
+diabetes_histograms.png,The variable Insulin can be seen as ordinal without losing information.,11078
+heart_histograms.png,The variable cp can be seen as ordinal without losing information.,3971
+Breast_Cancer_histograms.png,The variable perimeter_mean can be seen as ordinal without losing information.,12656
+adult_histograms.png,The variable hours-per-week can be seen as ordinal without losing information.,1590
+adult_histograms.png,The variable gender can be seen as ordinal without losing information.,1587
+heart_histograms.png,The variable exang can be seen as ordinal without losing information.,3977
+Wine_histograms.png,The variable Flavanoids can be seen as ordinal without losing information.,9595
+vehicle_histograms.png,The variable MAX LENGTH ASPECT RATIO can be seen as ordinal without losing information.,7441
+Breast_Cancer_histograms.png,The variable texture_worst can be seen as ordinal without losing information.,12663
+vehicle_histograms.png,The variable DISTANCE CIRCULARITY can be seen as ordinal without losing information.,7438
+Iris_histograms.png,The variable SepalWidthCm can be seen as ordinal without losing information.,7978
+vehicle_histograms.png,The variable RADIUS RATIO can be seen as ordinal without losing information.,7439
+vehicle_histograms.png,The variable CIRCULARITY can be seen as ordinal without losing information.,7437
+adult_histograms.png,The variable relationship can be seen as ordinal without losing information.,1585
+heart_histograms.png,The variable age can be seen as ordinal without losing information.,3969
+vehicle_histograms.png,The variable MINORKURTOSIS can be seen as ordinal without losing information.,7451
+Iris_histograms.png,The variable PetalWidthCm can be seen as ordinal without losing information.,7980
+diabetes_histograms.png,The variable Pregnancies can be seen as ordinal without losing information.,11074
+adult_histograms.png,The variable capital-gain can be seen as ordinal without losing information.,1588
+Breast_Cancer_histograms.png,The variable perimeter_worst can be seen as ordinal without losing information.,12664
+vehicle_histograms.png,The variable SCATTER RATIO can be seen as ordinal without losing information.,7442
+Breast_Cancer_histograms.png,The variable radius_worst can be seen as ordinal without losing information.,12662
+Wine_histograms.png,The variable Total phenols can be seen as ordinal without losing information.,9594
+Breast_Cancer_histograms.png,The variable texture_mean can be seen as ordinal without losing information.,12655
+Wine_histograms.png,The variable Malic acid can be seen as ordinal without losing information.,9591
+Wine_histograms.png,The variable Alcalinity of ash can be seen as ordinal without losing information.,9593
+vehicle_histograms.png,The variable MINORSKEWNESS can be seen as ordinal without losing information.,7450
+Wine_histograms.png,The variable Nonflavanoid phenols can be seen as ordinal without losing information.,9596
+vehicle_histograms.png,The variable LENGTHRECTANGULAR can be seen as ordinal without losing information.,7445
+Wine_histograms.png,The variable Proanthocyanins can be seen as ordinal without losing information.,9597
+Churn_Modelling_histograms.png,The variable Balance can be seen as ordinal without losing information.,2509
+Breast_Cancer_histograms.png,The variable perimeter_se can be seen as ordinal without losing information.,12658
+adult_histograms.png,The variable workclass can be seen as ordinal without losing information.,1579
+Churn_Modelling_histograms.png,The variable Age can be seen as ordinal without losing information.,2507
+vehicle_histograms.png,The variable MINORVARIANCE can be seen as ordinal without losing information.,7447
+heart_histograms.png,The variable trestbps can be seen as ordinal without losing information.,3972
+heart_histograms.png,The variable thal can be seen as ordinal without losing information.,3981
+Churn_Modelling_histograms.png,The variable HasCrCard can be seen as ordinal without losing information.,2511
+vehicle_histograms.png,The variable HOLLOWS RATIO can be seen as ordinal without losing information.,7453
+Iris_histograms.png,The variable PetalLengthCm can be seen as ordinal without losing information.,7979
+heart_histograms.png,The variable slope can be seen as ordinal without losing information.,3979
+adult_histograms.png,The variable occupation can be seen as ordinal without losing information.,1584
+Churn_Modelling_histograms.png,The variable Gender can be seen as ordinal without losing information.,2506
+vehicle_histograms.png,The variable ELONGATEDNESS can be seen as ordinal without losing information.,7443
+heart_histograms.png,The variable fbs can be seen as ordinal without losing information.,3974
+Breast_Cancer_histograms.png,The variable smoothness_se can be seen as ordinal without losing information.,12660
+Iris_histograms.png,The variable SepalLengthCm can be seen as ordinal without losing information.,7977
+Churn_Modelling_histograms.png,The variable IsActiveMember can be seen as ordinal without losing information.,2512
+BankNoteAuthentication_histograms.png,The variable skewness can be seen as ordinal without losing information.,10107
+Churn_Modelling_histograms.png,The variable CreditScore can be seen as ordinal without losing information.,2504
+adult_histograms.png,The variable education can be seen as ordinal without losing information.,1581
+BankNoteAuthentication_histograms.png,The variable entropy can be seen as ordinal without losing information.,10109
+Breast_Cancer_histograms.png,The variable area_se can be seen as ordinal without losing information.,12659
+diabetes_histograms.png,The variable Age can be seen as ordinal without losing information.,11081
+adult_histograms.png,The variable age can be seen as ordinal without losing information.,1578
+vehicle_histograms.png,The variable GYRATIONRADIUS can be seen as ordinal without losing information.,7448
+adult_histograms.png,The variable capital-loss can be seen as ordinal without losing information.,1589
+vehicle_boxplots.png,Variable MINORKURTOSIS is balanced.,7432
+Wine_histograms_numeric.png,Variable Alcalinity of ash is balanced.,9582
+heart_histograms_numeric.png,Variable thalach is balanced.,3964
+Churn_Modelling_histograms_numeric.png,Variable Balance is balanced.,2501
+Wine_histograms_numeric.png,Variable Class is balanced.,9578
+adult_boxplots.png,Variable age is balanced.,1572
+vehicle_histograms_numeric.png,Variable MINORSKEWNESS is balanced.,7431
+Wine_boxplots.png,Variable Proanthocyanins is balanced.,9586
+vehicle_boxplots.png,Variable PR AXISRECTANGULAR is balanced.,7425
+diabetes_boxplots.png,Variable SkinThickness is balanced.,11069
+Wine_boxplots.png,Variable Alcohol is balanced.,9579
+vehicle_boxplots.png,Variable PR AXIS ASPECT RATIO is balanced.,7421
+heart_boxplots.png,Variable chol is balanced.,3962
+vehicle_boxplots.png,Variable HOLLOWS RATIO is balanced.,7434
+heart_histograms_numeric.png,Variable oldpeak is balanced.,3965
+heart_boxplots.png,Variable trestbps is balanced.,3961
+adult_histograms_numeric.png,Variable fnlwgt is balanced.,1573
+diabetes_boxplots.png,Variable DiabetesPedigreeFunction is balanced.,11072
+Iris_boxplots.png,Variable SepalWidthCm is balanced.,7974
+vehicle_histograms_numeric.png,Variable LENGTHRECTANGULAR is balanced.,7426
+Iris_histograms_numeric.png,Variable PetalWidthCm is balanced.,7976
+Churn_Modelling_histograms_numeric.png,Variable CreditScore is balanced.,2498
+Wine_histograms_numeric.png,Variable Ash is balanced.,9581
+Churn_Modelling_boxplots.png,Variable EstimatedSalary is balanced.,2503
+adult_histograms_numeric.png,Variable educational-num is balanced.,1574
+Breast_Cancer_boxplots.png,Variable perimeter_mean is balanced.,12646
+adult_histograms_numeric.png,Variable hours-per-week is balanced.,1577
+Churn_Modelling_boxplots.png,Variable Age is balanced.,2499
+Wine_boxplots.png,Variable Flavanoids is balanced.,9584
+vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO is balanced.,7422
+Breast_Cancer_boxplots.png,Variable smoothness_se is balanced.,12650
+adult_histograms_numeric.png,Variable capital-loss is balanced.,1576
+diabetes_histograms_numeric.png,Variable BMI is balanced.,11071
+BankNoteAuthentication_boxplots.png,Variable curtosis is balanced.,10104
+diabetes_boxplots.png,Variable Glucose is balanced.,11067
+Iris_histograms_numeric.png,Variable SepalLengthCm is balanced.,7973
+BankNoteAuthentication_histograms_numeric.png,Variable skewness is balanced.,10103
+adult_histograms_numeric.png,Variable capital-gain is balanced.,1575
+vehicle_boxplots.png,Variable CIRCULARITY is balanced.,7418
+vehicle_boxplots.png,Variable MAJORKURTOSIS is balanced.,7433
+Breast_Cancer_histograms_numeric.png,Variable radius_worst is balanced.,12652
+vehicle_boxplots.png,Variable ELONGATEDNESS is balanced.,7424
+Iris_histograms_numeric.png,Variable PetalLengthCm is balanced.,7975
+Breast_Cancer_histograms_numeric.png,Variable texture_worst is balanced.,12653
+vehicle_boxplots.png,Variable GYRATIONRADIUS is balanced.,7429
+Breast_Cancer_histograms_numeric.png,Variable symmetry_se is balanced.,12651
+diabetes_boxplots.png,Variable Pregnancies is balanced.,11066
+heart_histograms_numeric.png,Variable restecg is balanced.,3963
+Wine_boxplots.png,Variable Nonflavanoid phenols is balanced.,9585
+BankNoteAuthentication_boxplots.png,Variable variance is balanced.,10102
+diabetes_histograms_numeric.png,Variable BloodPressure is balanced.,11068
+vehicle_histograms_numeric.png,Variable MAJORSKEWNESS is balanced.,7430
+Wine_boxplots.png,Variable Total phenols is balanced.,9583
+Breast_Cancer_histograms_numeric.png,Variable perimeter_worst is balanced.,12654
+heart_histograms_numeric.png,Variable thal is balanced.,3968
+vehicle_boxplots.png,Variable MAJORVARIANCE is balanced.,7427
+vehicle_histograms_numeric.png,Variable MINORVARIANCE is balanced.,7428
+diabetes_histograms_numeric.png,Variable Age is balanced.,11073
+Breast_Cancer_boxplots.png,Variable texture_mean is balanced.,12645
+BankNoteAuthentication_histograms_numeric.png,Variable entropy is balanced.,10105
+heart_boxplots.png,Variable slope is balanced.,3966
+Wine_boxplots.png,Variable Malic acid is balanced.,9580
+vehicle_histograms_numeric.png,Variable COMPACTNESS is balanced.,7417
+vehicle_boxplots.png,Variable target is balanced.,7435
+vehicle_boxplots.png,Variable RADIUS RATIO is balanced.,7420
+Churn_Modelling_boxplots.png,Variable Tenure is balanced.,2500
+Breast_Cancer_histograms_numeric.png,Variable perimeter_se is balanced.,12648
+Wine_histograms_numeric.png,Variable Color intensity is balanced.,9587
+vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY is balanced.,7419
+heart_boxplots.png,Variable ca is balanced.,3967
+Breast_Cancer_histograms_numeric.png,Variable texture_se is balanced.,12647
+Wine_histograms_numeric.png,Variable Hue is balanced.,9588
+Wine_boxplots.png,Variable OD280-OD315 of diluted wines is balanced.,9589
+Churn_Modelling_histograms_numeric.png,Variable NumOfProducts is balanced.,2502
+diabetes_histograms_numeric.png,Variable Insulin is balanced.,11070
+heart_boxplots.png,Variable cp is balanced.,3960
+Breast_Cancer_boxplots.png,Variable area_se is balanced.,12649
+heart_histograms_numeric.png,Variable age is balanced.,3959
+vehicle_boxplots.png,Variable SCATTER RATIO is balanced.,7423
+Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Total phenols.",9507
+Breast_Cancer_boxplots.png,"It is clear that variable perimeter_mean shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12609
+diabetes_boxplots.png,"It is clear that variable Pregnancies shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11030
+vehicle_decision_tree.png,It is clear that variable PR AXIS ASPECT RATIO is one of the two most relevant features.,5605
+Breast_Cancer_decision_tree.png,It is clear that variable texture_worst is one of the five most relevant features.,12133
+adult_histograms_numeric.png,"It is clear that variable capital-gain shows some outliers, but we can’t be sure of the same for variable educational-num.",1553
+diabetes_histograms_numeric.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable BMI.",11045
+vehicle_decision_tree.png,It is clear that variable DISTANCE CIRCULARITY is one of the three most relevant features.,5622
+vehicle_boxplots.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7307
+vehicle_decision_tree.png,It is clear that variable PR AXIS ASPECT RATIO is one of the three most relevant features.,5624
+Churn_Modelling_decision_tree.png,It is clear that variable Balance is one of the three most relevant features.,2173
+vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7215
+diabetes_decision_tree.png,It is clear that variable Glucose is one of the four most relevant features.,10721
+heart_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable chol.",3905
+vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7193
+vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7378
+Breast_Cancer_histograms_numeric.png,"It is clear that variable area_se shows some outliers, but we can’t be sure of the same for variable symmetry_se.",12612
+vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7260
+vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable target.",7398
+vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7346
+vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7297
+heart_boxplots.png,"It is clear that variable slope shows some outliers, but we can’t be sure of the same for variable trestbps.",3902
+Wine_histograms_numeric.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9517
+vehicle_decision_tree.png,It is clear that variable SCATTER RATIO is one of the two most relevant features.,5607
+heart_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable thalach.",3923
+Wine_histograms_numeric.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Alcohol.",9456
+Breast_Cancer_decision_tree.png,It is clear that variable texture_worst is one of the two most relevant features.,12103
+BankNoteAuthentication_decision_tree.png,It is clear that variable variance is one of the two most relevant features.,10014
+vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7182
+vehicle_decision_tree.png,It is clear that variable RADIUS RATIO is one of the four most relevant features.,5642
+Wine_decision_tree.png,It is clear that variable Alcohol is one of the four most relevant features.,8869
+Churn_Modelling_boxplots.png,"It is clear that variable NumOfProducts shows some outliers, but we can’t be sure of the same for variable Age.",2475
+vehicle_decision_tree.png,It is clear that variable MINORKURTOSIS is one of the five most relevant features.,5673
+diabetes_boxplots.png,"It is clear that variable BMI shows some outliers, but we can’t be sure of the same for variable Age.",11063
+heart_decision_tree.png,It is clear that variable restecg is one of the four most relevant features.,3322
+Breast_Cancer_decision_tree.png,It is clear that variable texture_se is one of the three most relevant features.,12107
+vehicle_decision_tree.png,It is clear that variable GYRATIONRADIUS is one of the four most relevant features.,5651
+heart_boxplots.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable thal.",3955
+vehicle_histograms_numeric.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7162
+Iris_boxplots.png,"It is clear that variable SepalLengthCm shows some outliers, but we can’t be sure of the same for variable PetalWidthCm.",7969
+adult_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable educational-num.",1551
+vehicle_decision_tree.png,It is clear that variable MINORSKEWNESS is one of the four most relevant features.,5653
+vehicle_histograms_numeric.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7078
+heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable oldpeak.",3932
+diabetes_decision_tree.png,It is clear that variable Pregnancies is one of the two most relevant features.,10704
+Churn_Modelling_histograms_numeric.png,"It is clear that variable Balance shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2490
+diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Insulin.",11043
+Breast_Cancer_boxplots.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable perimeter_worst.",12641
+vehicle_decision_tree.png,It is clear that variable HOLLOWS RATIO is one of the two most relevant features.,5618
+vehicle_decision_tree.png,It is clear that variable MAJORSKEWNESS is one of the four most relevant features.,5652
+diabetes_decision_tree.png,It is clear that variable Insulin is one of the three most relevant features.,10716
+diabetes_decision_tree.png,It is clear that variable BMI is one of the three most relevant features.,10717
+Wine_histograms_numeric.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9572
+heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable restecg.",3921
+vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7100
+BankNoteAuthentication_decision_tree.png,It is clear that variable entropy is one of the three most relevant features.,10021
+diabetes_decision_tree.png,It is clear that variable DiabetesPedigreeFunction is one of the four most relevant features.,10726
+heart_histograms_numeric.png,"It is clear that variable thalach shows some outliers, but we can’t be sure of the same for variable oldpeak.",3937
+vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7269
+Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Ash.",9478
+Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9514
+vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7231
+heart_boxplots.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable age.",3881
+adult_decision_tree.png,It is clear that variable capital-gain is one of the four most relevant features.,1121
+adult_decision_tree.png,It is clear that variable age is one of the four most relevant features.,1118
+vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7145
+vehicle_histograms_numeric.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7156
+diabetes_decision_tree.png,It is clear that variable BloodPressure is one of the two most relevant features.,10706
+heart_decision_tree.png,It is clear that variable slope is one of the two most relevant features.,3305
+vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7355
+heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable thal.",3957
+vehicle_histograms_numeric.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7179
+vehicle_decision_tree.png,It is clear that variable MINORVARIANCE is one of the four most relevant features.,5650
+adult_decision_tree.png,It is clear that variable hours-per-week is one of the five most relevant features.,1129
+Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12599
+vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7121
+vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7210
+adult_histograms_numeric.png,"It is clear that variable fnlwgt shows some outliers, but we can’t be sure of the same for variable capital-gain.",1557
+Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_se shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12583
+Iris_histograms_numeric.png,"It is clear that variable SepalLengthCm shows some outliers, but we can’t be sure of the same for variable PetalLengthCm.",7966
+Churn_Modelling_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2493
+Wine_decision_tree.png,It is clear that variable Class is one of the five most relevant features.,8880
+vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7245
+Breast_Cancer_decision_tree.png,It is clear that variable symmetry_se is one of the two most relevant features.,12101
+diabetes_boxplots.png,"It is clear that variable SkinThickness shows some outliers, but we can’t be sure of the same for variable BMI.",11047
+vehicle_decision_tree.png,It is clear that variable MAX LENGTH ASPECT RATIO is one of the two most relevant features.,5606
+heart_histograms_numeric.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable slope.",3948
+Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable smoothness_se.",12605
+vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7364
+adult_boxplots.png,"It is clear that variable capital-loss shows some outliers, but we can’t be sure of the same for variable age.",1544
+heart_histograms_numeric.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable thalach.",3928
+vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7146
+vehicle_decision_tree.png,It is clear that variable MAJORVARIANCE is one of the three most relevant features.,5630
+vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7190
+adult_decision_tree.png,It is clear that variable fnlwgt is one of the four most relevant features.,1119
+vehicle_decision_tree.png,It is clear that variable target is one of the two most relevant features.,5619
+vehicle_decision_tree.png,It is clear that variable PR AXISRECTANGULAR is one of the five most relevant features.,5666
+Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Color intensity.",9545
+diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11036
+vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7265
+vehicle_boxplots.png,"It is clear that variable MINORVARIANCE shows some outliers, but we can’t be sure of the same for variable GYRATIONRADIUS.",7301
+Breast_Cancer_boxplots.png,"It is clear that variable smoothness_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12567
+Wine_decision_tree.png,It is clear that variable Nonflavanoid phenols is one of the five most relevant features.,8887
+heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable restecg.",3914
+vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7257
+Churn_Modelling_histograms_numeric.png,"It is clear that variable EstimatedSalary shows some outliers, but we can’t be sure of the same for variable CreditScore.",2471
+Churn_Modelling_histograms_numeric.png,"It is clear that variable NumOfProducts shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2496
+heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable oldpeak.",3939
+heart_decision_tree.png,It is clear that variable ca is one of the four most relevant features.,3326
+vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable target.",7408
+Breast_Cancer_histograms_numeric.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable perimeter_se.",12588
+Breast_Cancer_decision_tree.png,It is clear that variable smoothness_se is one of the four most relevant features.,12120
+vehicle_decision_tree.png,It is clear that variable MAJORKURTOSIS is one of the two most relevant features.,5617
+diabetes_histograms_numeric.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable Insulin.",11038
+vehicle_histograms_numeric.png,"It is clear that variable MAJORVARIANCE shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7354
+vehicle_histograms_numeric.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7258
+Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9568
+Wine_boxplots.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9529
+BankNoteAuthentication_decision_tree.png,It is clear that variable skewness is one of the five most relevant features.,10027
+vehicle_decision_tree.png,It is clear that variable PR AXISRECTANGULAR is one of the three most relevant features.,5628
+vehicle_boxplots.png,"It is clear that variable PR AXIS ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7222
+vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7394
+Iris_histograms_numeric.png,"It is clear that variable SepalWidthCm shows some outliers, but we can’t be sure of the same for variable SepalLengthCm.",7960
+Iris_decision_tree.png,It is clear that variable SepalWidthCm is one of the five most relevant features.,7898
+vehicle_histograms_numeric.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7243
+adult_decision_tree.png,It is clear that variable capital-loss is one of the two most relevant features.,1110
+Wine_boxplots.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Malic acid.",9477
+Iris_boxplots.png,"It is clear that variable PetalWidthCm shows some outliers, but we can’t be sure of the same for variable SepalWidthCm.",7965
+vehicle_decision_tree.png,It is clear that variable MAJORVARIANCE is one of the two most relevant features.,5611
+Wine_histograms_numeric.png,"It is clear that variable Flavanoids shows some outliers, but we can’t be sure of the same for variable Proanthocyanins.",9539
+Breast_Cancer_decision_tree.png,It is clear that variable symmetry_se is one of the four most relevant features.,12121
+vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7279
+diabetes_boxplots.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11026
+diabetes_histograms_numeric.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11012
+Churn_Modelling_decision_tree.png,It is clear that variable Age is one of the four most relevant features.,2177
+diabetes_boxplots.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11024
+Breast_Cancer_decision_tree.png,It is clear that variable texture_worst is one of the three most relevant features.,12113
+diabetes_boxplots.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable Age.",11059
+vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7367
+BankNoteAuthentication_histograms_numeric.png,"It is clear that variable entropy shows some outliers, but we can’t be sure of the same for variable skewness.",10094
+heart_boxplots.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable chol.",3910
+Churn_Modelling_decision_tree.png,It is clear that variable EstimatedSalary is one of the four most relevant features.,2181
+Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9518
+Wine_decision_tree.png,It is clear that variable OD280-OD315 of diluted wines is one of the three most relevant features.,8867
+Churn_Modelling_decision_tree.png,It is clear that variable Balance is one of the two most relevant features.,2167
+Iris_decision_tree.png,It is clear that variable PetalLengthCm is one of the two most relevant features.,7887
+heart_decision_tree.png,It is clear that variable thalach is one of the five most relevant features.,3333
+BankNoteAuthentication_decision_tree.png,It is clear that variable entropy is one of the four most relevant features.,10025
+vehicle_decision_tree.png,It is clear that variable PR AXISRECTANGULAR is one of the four most relevant features.,5647
+vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7316
+vehicle_histograms_numeric.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7338
+diabetes_boxplots.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11029
+diabetes_decision_tree.png,It is clear that variable DiabetesPedigreeFunction is one of the three most relevant features.,10718
+Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable area_se.",12596
+vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7370
+diabetes_boxplots.png,"It is clear that variable Insulin shows some outliers, but we can’t be sure of the same for variable Age.",11062
+vehicle_boxplots.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7202
+BankNoteAuthentication_decision_tree.png,It is clear that variable skewness is one of the three most relevant features.,10019
+vehicle_decision_tree.png,It is clear that variable DISTANCE CIRCULARITY is one of the two most relevant features.,5603
+vehicle_decision_tree.png,It is clear that variable ELONGATEDNESS is one of the three most relevant features.,5627
+vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7147
+diabetes_boxplots.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable SkinThickness.",11035
+Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9524
+vehicle_boxplots.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7345
+vehicle_histograms_numeric.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7142
+vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7313
+heart_decision_tree.png,It is clear that variable ca is one of the three most relevant features.,3316
+vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7211
+Churn_Modelling_histograms_numeric.png,"It is clear that variable Balance shows some outliers, but we can’t be sure of the same for variable CreditScore.",2469
+heart_histograms_numeric.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable chol.",3913
+vehicle_boxplots.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7167
+vehicle_decision_tree.png,It is clear that variable MAX LENGTH ASPECT RATIO is one of the three most relevant features.,5625
+adult_histograms_numeric.png,"It is clear that variable hours-per-week shows some outliers, but we can’t be sure of the same for variable capital-loss.",1565
+vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7325
+vehicle_histograms_numeric.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7248
+vehicle_boxplots.png,"It is clear that variable ELONGATEDNESS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7116
+BankNoteAuthentication_boxplots.png,"It is clear that variable curtosis shows some outliers, but we can’t be sure of the same for variable variance.",10090
+vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MINORKURTOSIS.",7358
+Iris_boxplots.png,"It is clear that variable SepalWidthCm shows some outliers, but we can’t be sure of the same for variable PetalLengthCm.",7967
+BankNoteAuthentication_histograms_numeric.png,"It is clear that variable skewness shows some outliers, but we can’t be sure of the same for variable variance.",10089
+Breast_Cancer_decision_tree.png,It is clear that variable perimeter_se is one of the three most relevant features.,12108
+Wine_decision_tree.png,It is clear that variable Flavanoids is one of the three most relevant features.,8862
+vehicle_histograms_numeric.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORKURTOSIS.",7371
+Wine_histograms_numeric.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Total phenols.",9509
+vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7205
+Breast_Cancer_boxplots.png,"It is clear that variable texture_mean shows some outliers, but we can’t be sure of the same for variable radius_worst.",12617
+diabetes_boxplots.png,"It is clear that variable BloodPressure shows some outliers, but we can’t be sure of the same for variable BMI.",11046
+heart_histograms_numeric.png,"It is clear that variable cp shows some outliers, but we can’t be sure of the same for variable thalach.",3924
+heart_histograms_numeric.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable slope.",3947
+adult_boxplots.png,"It is clear that variable capital-gain shows some outliers, but we can’t be sure of the same for variable capital-loss.",1564
+vehicle_decision_tree.png,It is clear that variable HOLLOWS RATIO is one of the five most relevant features.,5675
+diabetes_decision_tree.png,It is clear that variable BloodPressure is one of the three most relevant features.,10714
+diabetes_boxplots.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable BloodPressure.",11028
+diabetes_histograms_numeric.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11014
+vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7082
+Wine_decision_tree.png,It is clear that variable Flavanoids is one of the five most relevant features.,8886
+Wine_histograms_numeric.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9527
+vehicle_histograms_numeric.png,"It is clear that variable DISTANCE CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7256
+Wine_histograms_numeric.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Nonflavanoid phenols.",9522
+heart_histograms_numeric.png,"It is clear that variable thal shows some outliers, but we can’t be sure of the same for variable thalach.",3931
+vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7083
+vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable target.",7412
+heart_histograms_numeric.png,"It is clear that variable chol shows some outliers, but we can’t be sure of the same for variable trestbps.",3898
+Churn_Modelling_boxplots.png,"It is clear that variable Tenure shows some outliers, but we can’t be sure of the same for variable Age.",2473
+adult_boxplots.png,"It is clear that variable fnlwgt shows some outliers, but we can’t be sure of the same for variable age.",1541
+vehicle_decision_tree.png,It is clear that variable MAJORSKEWNESS is one of the five most relevant features.,5671
+vehicle_decision_tree.png,It is clear that variable target is one of the three most relevant features.,5638
+Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12565
+adult_decision_tree.png,It is clear that variable fnlwgt is one of the two most relevant features.,1107
+Wine_decision_tree.png,It is clear that variable Color intensity is one of the two most relevant features.,8853
+Churn_Modelling_decision_tree.png,It is clear that variable CreditScore is one of the five most relevant features.,2182
+vehicle_decision_tree.png,It is clear that variable MAJORKURTOSIS is one of the four most relevant features.,5655
+Wine_boxplots.png,"It is clear that variable Nonflavanoid phenols shows some outliers, but we can’t be sure of the same for variable Color intensity.",9551
+Breast_Cancer_boxplots.png,"It is clear that variable texture_worst shows some outliers, but we can’t be sure of the same for variable texture_mean.",12561
+adult_decision_tree.png,It is clear that variable age is one of the three most relevant features.,1112
+vehicle_boxplots.png,"It is clear that variable MAJORKURTOSIS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7323
+diabetes_histograms_numeric.png,"It is clear that variable Glucose shows some outliers, but we can’t be sure of the same for variable Pregnancies.",11009
+heart_decision_tree.png,It is clear that variable oldpeak is one of the five most relevant features.,3334
+vehicle_histograms_numeric.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7272
+Wine_decision_tree.png,It is clear that variable Alcohol is one of the three most relevant features.,8857
+Wine_decision_tree.png,It is clear that variable Alcalinity of ash is one of the five most relevant features.,8884
+vehicle_boxplots.png,"It is clear that variable HOLLOWS RATIO shows some outliers, but we can’t be sure of the same for variable MAX LENGTH ASPECT RATIO.",7180
+Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable OD280-OD315 of diluted wines.",9571
+Churn_Modelling_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2488
+diabetes_histograms_numeric.png,"It is clear that variable DiabetesPedigreeFunction shows some outliers, but we can’t be sure of the same for variable BMI.",11049
+Iris_decision_tree.png,It is clear that variable SepalWidthCm is one of the four most relevant features.,7894
+Wine_decision_tree.png,It is clear that variable Class is one of the four most relevant features.,8868
+Wine_histograms_numeric.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Ash.",9479
+vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7114
+vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable CIRCULARITY.",7103
+vehicle_boxplots.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable ELONGATEDNESS.",7207
+Wine_histograms_numeric.png,"It is clear that variable Alcalinity of ash shows some outliers, but we can’t be sure of the same for variable Class.",9448
+vehicle_boxplots.png,"It is clear that variable SCATTER RATIO shows some outliers, but we can’t be sure of the same for variable MINORSKEWNESS.",7332
+Breast_Cancer_histograms_numeric.png,"It is clear that variable radius_worst shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12569
+heart_decision_tree.png,It is clear that variable ca is one of the two most relevant features.,3306
+heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable slope.",3941
+Wine_decision_tree.png,It is clear that variable Nonflavanoid phenols is one of the three most relevant features.,8863
+Breast_Cancer_histograms_numeric.png,"It is clear that variable perimeter_worst shows some outliers, but we can’t be sure of the same for variable texture_mean.",12562
+vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7321
+Wine_decision_tree.png,It is clear that variable Alcalinity of ash is one of the three most relevant features.,8860
+Wine_histograms_numeric.png,"It is clear that variable Malic acid shows some outliers, but we can’t be sure of the same for variable Alcohol.",9457
+Iris_decision_tree.png,It is clear that variable PetalLengthCm is one of the four most relevant features.,7895
+Churn_Modelling_decision_tree.png,It is clear that variable Age is one of the five most relevant features.,2183
+Breast_Cancer_decision_tree.png,It is clear that variable texture_se is one of the five most relevant features.,12127
+vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable SCATTER RATIO.",7195
+heart_boxplots.png,"It is clear that variable ca shows some outliers, but we can’t be sure of the same for variable trestbps.",3903
+Wine_boxplots.png,"It is clear that variable Total phenols shows some outliers, but we can’t be sure of the same for variable Hue.",9560
+Breast_Cancer_decision_tree.png,It is clear that variable symmetry_se is one of the five most relevant features.,12131
+Breast_Cancer_decision_tree.png,It is clear that variable perimeter_mean is one of the two most relevant features.,12096
+Breast_Cancer_decision_tree.png,It is clear that variable area_se is one of the two most relevant features.,12099
+vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7253
+vehicle_boxplots.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7141
+Wine_histograms_numeric.png,"It is clear that variable Proanthocyanins shows some outliers, but we can’t be sure of the same for variable Alcohol.",9463
+vehicle_histograms_numeric.png,"It is clear that variable PR AXISRECTANGULAR shows some outliers, but we can’t be sure of the same for variable PR AXIS ASPECT RATIO.",7153
+heart_decision_tree.png,It is clear that variable ca is one of the five most relevant features.,3336
+Churn_Modelling_decision_tree.png,It is clear that variable EstimatedSalary is one of the five most relevant features.,2187
+vehicle_boxplots.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable LENGTHRECTANGULAR.",7241
+Wine_histograms_numeric.png,"It is clear that variable OD280-OD315 of diluted wines shows some outliers, but we can’t be sure of the same for variable Color intensity.",9554
+Wine_decision_tree.png,It is clear that variable Malic acid is one of the four most relevant features.,8870
+vehicle_boxplots.png,"It is clear that variable GYRATIONRADIUS shows some outliers, but we can’t be sure of the same for variable RADIUS RATIO.",7139
+heart_boxplots.png,"It is clear that variable oldpeak shows some outliers, but we can’t be sure of the same for variable restecg.",3919
+Breast_Cancer_decision_tree.png,It is clear that variable perimeter_worst is one of the two most relevant features.,12104
+Wine_decision_tree.png,It is clear that variable Flavanoids is one of the four most relevant features.,8874
+vehicle_boxplots.png,"It is clear that variable COMPACTNESS shows some outliers, but we can’t be sure of the same for variable PR AXISRECTANGULAR.",7218
+Churn_Modelling_boxplots.png,"It is clear that variable EstimatedSalary shows some outliers, but we can’t be sure of the same for variable NumOfProducts.",2491
+vehicle_boxplots.png,"It is clear that variable LENGTHRECTANGULAR shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7317
+adult_histograms_numeric.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable hours-per-week.",1566
+Breast_Cancer_histograms_numeric.png,"It is clear that variable symmetry_se shows some outliers, but we can’t be sure of the same for variable perimeter_mean.",12568
+vehicle_decision_tree.png,It is clear that variable COMPACTNESS is one of the three most relevant features.,5620
+Churn_Modelling_histograms_numeric.png,"It is clear that variable CreditScore shows some outliers, but we can’t be sure of the same for variable Age.",2472
+heart_boxplots.png,"It is clear that variable age shows some outliers, but we can’t be sure of the same for variable cp.",3887
+heart_boxplots.png,"It is clear that variable restecg shows some outliers, but we can’t be sure of the same for variable oldpeak.",3936
+Wine_boxplots.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Alcalinity of ash.",9498
+Churn_Modelling_histograms_numeric.png,"It is clear that variable Age shows some outliers, but we can’t be sure of the same for variable Tenure.",2478
+Wine_boxplots.png,"It is clear that variable Alcohol shows some outliers, but we can’t be sure of the same for variable Flavanoids.",9512
+Wine_histograms_numeric.png,"It is clear that variable Hue shows some outliers, but we can’t be sure of the same for variable Malic acid.",9476
+vehicle_histograms_numeric.png,"It is clear that variable CIRCULARITY shows some outliers, but we can’t be sure of the same for variable MAJORSKEWNESS.",7309
+vehicle_boxplots.png,"It is clear that variable MINORKURTOSIS shows some outliers, but we can’t be sure of the same for variable HOLLOWS RATIO.",7395
+Wine_boxplots.png,"It is clear that variable Class shows some outliers, but we can’t be sure of the same for variable Malic acid.",9467
+vehicle_decision_tree.png,It is clear that variable MINORSKEWNESS is one of the five most relevant features.,5672
+vehicle_boxplots.png,"It is clear that variable MAJORSKEWNESS shows some outliers, but we can’t be sure of the same for variable DISTANCE CIRCULARITY.",7122
+heart_decision_tree.png,It is clear that variable oldpeak is one of the four most relevant features.,3324
+vehicle_histograms_numeric.png,"It is clear that variable target shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7289
+heart_decision_tree.png,It is clear that variable cp is one of the four most relevant features.,3319
+vehicle_histograms_numeric.png,"It is clear that variable MAX LENGTH ASPECT RATIO shows some outliers, but we can’t be sure of the same for variable COMPACTNESS.",7079
+Churn_Modelling_histograms_numeric.png,"It is clear that variable Tenure shows some outliers, but we can’t be sure of the same for variable EstimatedSalary.",2494
+Wine_histograms_numeric.png,"It is clear that variable Ash shows some outliers, but we can’t be sure of the same for variable Total phenols.",9503
+vehicle_histograms_numeric.png,"It is clear that variable RADIUS RATIO shows some outliers, but we can’t be sure of the same for variable MINORVARIANCE.",7275
+vehicle_histograms_numeric.png,"It is clear that variable MINORSKEWNESS shows some outliers, but we can’t be sure of the same for variable MAJORVARIANCE.",7267
+heart_decision_tree.png,It is clear that variable age is one of the two most relevant features.,3298
+Wine_boxplots.png,Variable Total phenols shows a high number of outlier values.,9437
+Breast_Cancer_histograms_numeric.png,Variable perimeter_se shows a high number of outlier values.,12546
+Breast_Cancer_boxplots.png,Variable perimeter_worst shows some outlier values.,12542
+Wine_boxplots.png,Variable Flavanoids shows some outlier values.,9426
+Breast_Cancer_histograms_numeric.png,Variable texture_worst shows a high number of outlier values.,12551
+vehicle_boxplots.png,Variable COMPACTNESS shows a high number of outlier values.,7055
+Wine_histograms_numeric.png,Variable Flavanoids shows a high number of outlier values.,9438
+diabetes_boxplots.png,Variable Pregnancies shows some outlier values.,10992
+Wine_histograms_numeric.png,Variable Nonflavanoid phenols shows some outlier values.,9427
+vehicle_boxplots.png,Variable PR AXISRECTANGULAR shows some outlier values.,7044
+Churn_Modelling_histograms_numeric.png,Variable Tenure shows a high number of outlier values.,2462
+vehicle_histograms_numeric.png,Variable MINORVARIANCE shows some outlier values.,7047
+heart_histograms_numeric.png,Variable oldpeak shows a high number of outlier values.,3873
+diabetes_histograms_numeric.png,Variable BloodPressure shows some outlier values.,10994
+Breast_Cancer_histograms_numeric.png,Variable texture_se shows some outlier values.,12535
+Iris_histograms_numeric.png,Variable SepalLengthCm shows some outlier values.,7951
+Wine_histograms_numeric.png,Variable OD280-OD315 of diluted wines shows a high number of outlier values.,9443
+vehicle_histograms_numeric.png,Variable MAJORKURTOSIS shows a high number of outlier values.,7071
+vehicle_boxplots.png,Variable MINORSKEWNESS shows some outlier values.,7050
+vehicle_boxplots.png,Variable RADIUS RATIO shows a high number of outlier values.,7058
+vehicle_histograms_numeric.png,Variable ELONGATEDNESS shows a high number of outlier values.,7062
+heart_histograms_numeric.png,Variable thal shows some outlier values.,3866
+Iris_boxplots.png,Variable PetalLengthCm shows a high number of outlier values.,7957
+Breast_Cancer_histograms_numeric.png,Variable symmetry_se shows some outlier values.,12539
+Churn_Modelling_histograms_numeric.png,Variable Balance shows some outlier values.,2457
+diabetes_histograms_numeric.png,Variable SkinThickness shows a high number of outlier values.,11003
+Churn_Modelling_boxplots.png,Variable CreditScore shows some outlier values.,2454
+diabetes_boxplots.png,Variable Glucose shows some outlier values.,10993
+diabetes_histograms_numeric.png,Variable BMI shows a high number of outlier values.,11005
+heart_boxplots.png,Variable oldpeak shows some outlier values.,3863
+Iris_boxplots.png,Variable SepalLengthCm shows a high number of outlier values.,7955
+vehicle_boxplots.png,Variable MINORVARIANCE shows a high number of outlier values.,7066
+Wine_histograms_numeric.png,Variable Proanthocyanins shows some outlier values.,9428
+Wine_boxplots.png,Variable Hue shows some outlier values.,9430
+vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO shows a high number of outlier values.,7060
+vehicle_histograms_numeric.png,Variable LENGTHRECTANGULAR shows some outlier values.,7045
+Wine_boxplots.png,Variable Class shows a high number of outlier values.,9432
+Breast_Cancer_boxplots.png,Variable area_se shows a high number of outlier values.,12547
+Breast_Cancer_boxplots.png,Variable texture_worst shows some outlier values.,12541
+adult_histograms_numeric.png,Variable capital-loss shows some outlier values.,1532
+Wine_histograms_numeric.png,Variable Class shows some outlier values.,9420
+diabetes_histograms_numeric.png,Variable Pregnancies shows a high number of outlier values.,11000
+Churn_Modelling_histograms_numeric.png,Variable NumOfProducts shows some outlier values.,2458
+heart_boxplots.png,Variable trestbps shows some outlier values.,3859
+vehicle_boxplots.png,Variable CIRCULARITY shows a high number of outlier values.,7056
+vehicle_histograms_numeric.png,Variable MINORKURTOSIS shows some outlier values.,7051
+BankNoteAuthentication_histograms_numeric.png,Variable skewness shows a high number of outlier values.,10085
+Wine_histograms_numeric.png,Variable Malic acid shows a high number of outlier values.,9434
+vehicle_histograms_numeric.png,Variable LENGTHRECTANGULAR shows a high number of outlier values.,7064
+Wine_boxplots.png,Variable Hue shows a high number of outlier values.,9442
+vehicle_histograms_numeric.png,Variable SCATTER RATIO shows a high number of outlier values.,7061
+diabetes_boxplots.png,Variable Age shows some outlier values.,10999
+Breast_Cancer_boxplots.png,Variable texture_mean shows some outlier values.,12533
+diabetes_boxplots.png,Variable Age shows a high number of outlier values.,11007
+adult_histograms_numeric.png,Variable age shows a high number of outlier values.,1534
+vehicle_histograms_numeric.png,Variable MAJORSKEWNESS shows some outlier values.,7049
+Breast_Cancer_boxplots.png,Variable symmetry_se shows a high number of outlier values.,12549
+Iris_boxplots.png,Variable PetalLengthCm shows some outlier values.,7953
+heart_boxplots.png,Variable thal shows a high number of outlier values.,3876
+Wine_boxplots.png,Variable Total phenols shows some outlier values.,9425
+Wine_boxplots.png,Variable Alcohol shows a high number of outlier values.,9433
+vehicle_histograms_numeric.png,Variable HOLLOWS RATIO shows some outlier values.,7053
+vehicle_histograms_numeric.png,Variable GYRATIONRADIUS shows a high number of outlier values.,7067
+BankNoteAuthentication_boxplots.png,Variable variance shows some outlier values.,10080
+vehicle_histograms_numeric.png,Variable HOLLOWS RATIO shows a high number of outlier values.,7072
+heart_boxplots.png,Variable restecg shows some outlier values.,3861
+BankNoteAuthentication_histograms_numeric.png,Variable entropy shows some outlier values.,10083
+Churn_Modelling_histograms_numeric.png,Variable Balance shows a high number of outlier values.,2463
+adult_boxplots.png,Variable educational-num shows a high number of outlier values.,1536
+vehicle_boxplots.png,Variable MINORSKEWNESS shows a high number of outlier values.,7069
+diabetes_histograms_numeric.png,Variable BMI shows some outlier values.,10997
+vehicle_histograms_numeric.png,Variable PR AXISRECTANGULAR shows a high number of outlier values.,7063
+Breast_Cancer_histograms_numeric.png,Variable texture_se shows a high number of outlier values.,12545
+vehicle_histograms_numeric.png,Variable GYRATIONRADIUS shows some outlier values.,7048
+adult_boxplots.png,Variable capital-gain shows some outlier values.,1531
+vehicle_boxplots.png,Variable target shows a high number of outlier values.,7073
+vehicle_histograms_numeric.png,Variable SCATTER RATIO shows some outlier values.,7042
+Breast_Cancer_boxplots.png,Variable smoothness_se shows some outlier values.,12538
+BankNoteAuthentication_boxplots.png,Variable curtosis shows some outlier values.,10082
+adult_histograms_numeric.png,Variable educational-num shows some outlier values.,1530
+heart_histograms_numeric.png,Variable trestbps shows a high number of outlier values.,3869
+heart_histograms_numeric.png,Variable thalach shows a high number of outlier values.,3872
+Iris_histograms_numeric.png,Variable PetalWidthCm shows some outlier values.,7954
+diabetes_histograms_numeric.png,Variable DiabetesPedigreeFunction shows a high number of outlier values.,11006
+Wine_histograms_numeric.png,Variable Alcalinity of ash shows a high number of outlier values.,9436
+diabetes_boxplots.png,Variable BloodPressure shows a high number of outlier values.,11002
+vehicle_histograms_numeric.png,Variable COMPACTNESS shows some outlier values.,7036
+Breast_Cancer_histograms_numeric.png,Variable smoothness_se shows a high number of outlier values.,12548
+vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY shows some outlier values.,7038
+adult_histograms_numeric.png,Variable hours-per-week shows a high number of outlier values.,1539
+Breast_Cancer_histograms_numeric.png,Variable perimeter_mean shows a high number of outlier values.,12544
+Wine_histograms_numeric.png,Variable Alcohol shows some outlier values.,9421
+Churn_Modelling_histograms_numeric.png,Variable Age shows some outlier values.,2455
+diabetes_histograms_numeric.png,Variable SkinThickness shows some outlier values.,10995
+vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY shows a high number of outlier values.,7057
+adult_histograms_numeric.png,Variable fnlwgt shows some outlier values.,1529
+adult_boxplots.png,Variable fnlwgt shows a high number of outlier values.,1535
+heart_boxplots.png,Variable chol shows some outlier values.,3860
+Wine_histograms_numeric.png,Variable OD280-OD315 of diluted wines shows some outlier values.,9431
+vehicle_histograms_numeric.png,Variable target shows some outlier values.,7054
+adult_boxplots.png,Variable hours-per-week shows some outlier values.,1533
+BankNoteAuthentication_boxplots.png,Variable curtosis shows a high number of outlier values.,10086
+vehicle_boxplots.png,Variable MAJORVARIANCE shows a high number of outlier values.,7065
+vehicle_boxplots.png,Variable MAJORKURTOSIS shows some outlier values.,7052
+diabetes_boxplots.png,Variable DiabetesPedigreeFunction shows some outlier values.,10998
+vehicle_histograms_numeric.png,Variable RADIUS RATIO shows some outlier values.,7039
+Breast_Cancer_histograms_numeric.png,Variable area_se shows some outlier values.,12537
+Churn_Modelling_histograms_numeric.png,Variable EstimatedSalary shows a high number of outlier values.,2465
+diabetes_histograms_numeric.png,Variable Glucose shows a high number of outlier values.,11001
+adult_boxplots.png,Variable capital-gain shows a high number of outlier values.,1537
+Breast_Cancer_boxplots.png,Variable radius_worst shows a high number of outlier values.,12550
+Breast_Cancer_boxplots.png,Variable perimeter_worst shows a high number of outlier values.,12552
+heart_boxplots.png,Variable ca shows a high number of outlier values.,3875
+Wine_boxplots.png,Variable Ash shows a high number of outlier values.,9435
+vehicle_histograms_numeric.png,Variable CIRCULARITY shows some outlier values.,7037
+Churn_Modelling_histograms_numeric.png,Variable Tenure shows some outlier values.,2456
+heart_boxplots.png,Variable age shows some outlier values.,3857
+Wine_boxplots.png,Variable Nonflavanoid phenols shows a high number of outlier values.,9439
+Wine_histograms_numeric.png,Variable Color intensity shows a high number of outlier values.,9441
+Churn_Modelling_histograms_numeric.png,Variable CreditScore shows a high number of outlier values.,2460
+adult_boxplots.png,Variable capital-loss shows a high number of outlier values.,1538
+Churn_Modelling_histograms_numeric.png,Variable Age shows a high number of outlier values.,2461
+BankNoteAuthentication_boxplots.png,Variable entropy shows a high number of outlier values.,10087
+heart_histograms_numeric.png,Variable cp shows a high number of outlier values.,3868
+Wine_boxplots.png,Variable Color intensity shows some outlier values.,9429
+BankNoteAuthentication_boxplots.png,Variable variance shows a high number of outlier values.,10084
+Churn_Modelling_boxplots.png,Variable EstimatedSalary shows some outlier values.,2459
+Breast_Cancer_boxplots.png,Variable perimeter_se shows some outlier values.,12536
+diabetes_boxplots.png,Variable Insulin shows some outlier values.,10996
+vehicle_histograms_numeric.png,Variable ELONGATEDNESS shows some outlier values.,7043
+Iris_boxplots.png,Variable SepalWidthCm shows a high number of outlier values.,7956
+vehicle_boxplots.png,Variable MINORKURTOSIS shows a high number of outlier values.,7070
+Wine_histograms_numeric.png,Variable Alcalinity of ash shows some outlier values.,9424
+BankNoteAuthentication_histograms_numeric.png,Variable skewness shows some outlier values.,10081
+adult_boxplots.png,Variable age shows some outlier values.,1528
+vehicle_histograms_numeric.png,Variable MAJORSKEWNESS shows a high number of outlier values.,7068
+heart_histograms_numeric.png,Variable ca shows some outlier values.,3865
+heart_histograms_numeric.png,Variable cp shows some outlier values.,3858
+Iris_histograms_numeric.png,Variable SepalWidthCm shows some outlier values.,7952
+Iris_histograms_numeric.png,Variable PetalWidthCm shows a high number of outlier values.,7958
+vehicle_histograms_numeric.png,Variable MAJORVARIANCE shows some outlier values.,7046
+heart_histograms_numeric.png,Variable restecg shows a high number of outlier values.,3871
+vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO shows some outlier values.,7041
+Wine_histograms_numeric.png,Variable Ash shows some outlier values.,9423
+heart_boxplots.png,Variable age shows a high number of outlier values.,3867
+vehicle_boxplots.png,Variable PR AXIS ASPECT RATIO shows some outlier values.,7040
+Churn_Modelling_histograms_numeric.png,Variable NumOfProducts shows a high number of outlier values.,2464
+vehicle_boxplots.png,Variable PR AXIS ASPECT RATIO shows a high number of outlier values.,7059
+heart_histograms_numeric.png,Variable chol shows a high number of outlier values.,3870
+Wine_histograms_numeric.png,Variable Proanthocyanins shows a high number of outlier values.,9440
+heart_histograms_numeric.png,Variable slope shows some outlier values.,3864
+heart_histograms_numeric.png,Variable thalach shows some outlier values.,3862
+Breast_Cancer_histograms_numeric.png,Variable perimeter_mean shows some outlier values.,12534
+Breast_Cancer_histograms_numeric.png,Variable texture_mean shows a high number of outlier values.,12543
+diabetes_histograms_numeric.png,Variable Insulin shows a high number of outlier values.,11004
+Breast_Cancer_boxplots.png,Variable radius_worst shows some outlier values.,12540
+Wine_histograms_numeric.png,Variable Malic acid shows some outlier values.,9422
+heart_histograms_numeric.png,Variable slope shows a high number of outlier values.,3874
+Iris_boxplots.png,Variable SepalWidthCm doesn’t have any outliers.,7948
+vehicle_histograms_numeric.png,Variable CIRCULARITY doesn’t have any outliers.,7018
+diabetes_boxplots.png,Variable SkinThickness doesn’t have any outliers.,10987
+diabetes_boxplots.png,Variable BloodPressure doesn’t have any outliers.,10986
+vehicle_boxplots.png,Variable PR AXISRECTANGULAR doesn’t have any outliers.,7025
+adult_histograms_numeric.png,Variable age doesn’t have any outliers.,1522
+Churn_Modelling_histograms_numeric.png,Variable NumOfProducts doesn’t have any outliers.,2452
+vehicle_histograms_numeric.png,Variable target doesn’t have any outliers.,7035
+vehicle_boxplots.png,Variable HOLLOWS RATIO doesn’t have any outliers.,7034
+Wine_histograms_numeric.png,Variable OD280-OD315 of diluted wines doesn’t have any outliers.,9419
+heart_boxplots.png,Variable cp doesn’t have any outliers.,3848
+Breast_Cancer_boxplots.png,Variable perimeter_worst doesn’t have any outliers.,12532
+Wine_histograms_numeric.png,Variable Total phenols doesn’t have any outliers.,9413
+diabetes_boxplots.png,Variable Insulin doesn’t have any outliers.,10988
+vehicle_boxplots.png,Variable MAJORKURTOSIS doesn’t have any outliers.,7033
+vehicle_histograms_numeric.png,Variable PR AXIS ASPECT RATIO doesn’t have any outliers.,7021
+heart_boxplots.png,Variable slope doesn’t have any outliers.,3854
+BankNoteAuthentication_boxplots.png,Variable entropy doesn’t have any outliers.,10079
+Wine_histograms_numeric.png,Variable Flavanoids doesn’t have any outliers.,9414
+Breast_Cancer_histograms_numeric.png,Variable area_se doesn’t have any outliers.,12527
+Wine_histograms_numeric.png,Variable Malic acid doesn’t have any outliers.,9410
+diabetes_boxplots.png,Variable DiabetesPedigreeFunction doesn’t have any outliers.,10990
+Churn_Modelling_histograms_numeric.png,Variable Age doesn’t have any outliers.,2449
+Wine_boxplots.png,Variable Nonflavanoid phenols doesn’t have any outliers.,9415
+Breast_Cancer_boxplots.png,Variable radius_worst doesn’t have any outliers.,12530
+Iris_histograms_numeric.png,Variable PetalWidthCm doesn’t have any outliers.,7950
+vehicle_boxplots.png,Variable RADIUS RATIO doesn’t have any outliers.,7020
+adult_boxplots.png,Variable capital-gain doesn’t have any outliers.,1525
+vehicle_boxplots.png,Variable LENGTHRECTANGULAR doesn’t have any outliers.,7026
+Breast_Cancer_boxplots.png,Variable perimeter_mean doesn’t have any outliers.,12524
+vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO doesn’t have any outliers.,7022
+heart_boxplots.png,Variable trestbps doesn’t have any outliers.,3849
+BankNoteAuthentication_boxplots.png,Variable skewness doesn’t have any outliers.,10077
+adult_boxplots.png,Variable hours-per-week doesn’t have any outliers.,1527
+heart_histograms_numeric.png,Variable oldpeak doesn’t have any outliers.,3853
+vehicle_boxplots.png,Variable MAJORVARIANCE doesn’t have any outliers.,7027
+heart_boxplots.png,Variable restecg doesn’t have any outliers.,3851
+Wine_histograms_numeric.png,Variable Alcalinity of ash doesn’t have any outliers.,9412
+vehicle_histograms_numeric.png,Variable MINORKURTOSIS doesn’t have any outliers.,7032
+Breast_Cancer_boxplots.png,Variable texture_se doesn’t have any outliers.,12525
+Wine_histograms_numeric.png,Variable Class doesn’t have any outliers.,9408
+adult_boxplots.png,Variable capital-loss doesn’t have any outliers.,1526
+Iris_histograms_numeric.png,Variable SepalLengthCm doesn’t have any outliers.,7947
+heart_boxplots.png,Variable thal doesn’t have any outliers.,3856
+diabetes_histograms_numeric.png,Variable BMI doesn’t have any outliers.,10989
+heart_boxplots.png,Variable ca doesn’t have any outliers.,3855
+Wine_histograms_numeric.png,Variable Proanthocyanins doesn’t have any outliers.,9416
+Breast_Cancer_histograms_numeric.png,Variable texture_mean doesn’t have any outliers.,12523
+Churn_Modelling_boxplots.png,Variable CreditScore doesn’t have any outliers.,2448
+vehicle_histograms_numeric.png,Variable MINORVARIANCE doesn’t have any outliers.,7028
+vehicle_histograms_numeric.png,Variable MINORSKEWNESS doesn’t have any outliers.,7031
+heart_boxplots.png,Variable chol doesn’t have any outliers.,3850
+Breast_Cancer_boxplots.png,Variable symmetry_se doesn’t have any outliers.,12529
+Churn_Modelling_histograms_numeric.png,Variable Balance doesn’t have any outliers.,2451
+Churn_Modelling_histograms_numeric.png,Variable EstimatedSalary doesn’t have any outliers.,2453
+vehicle_boxplots.png,Variable GYRATIONRADIUS doesn’t have any outliers.,7029
+Churn_Modelling_boxplots.png,Variable Tenure doesn’t have any outliers.,2450
+Breast_Cancer_boxplots.png,Variable texture_worst doesn’t have any outliers.,12531
+vehicle_boxplots.png,Variable MAJORSKEWNESS doesn’t have any outliers.,7030
+Wine_boxplots.png,Variable Color intensity doesn’t have any outliers.,9417
+vehicle_boxplots.png,Variable COMPACTNESS doesn’t have any outliers.,7017
+diabetes_histograms_numeric.png,Variable Glucose doesn’t have any outliers.,10985
+BankNoteAuthentication_boxplots.png,Variable curtosis doesn’t have any outliers.,10078
+Wine_histograms_numeric.png,Variable Hue doesn’t have any outliers.,9418
+adult_histograms_numeric.png,Variable educational-num doesn’t have any outliers.,1524
+diabetes_histograms_numeric.png,Variable Pregnancies doesn’t have any outliers.,10984
+Iris_histograms_numeric.png,Variable PetalLengthCm doesn’t have any outliers.,7949
+Wine_histograms_numeric.png,Variable Ash doesn’t have any outliers.,9411
+heart_histograms_numeric.png,Variable thalach doesn’t have any outliers.,3852
+BankNoteAuthentication_boxplots.png,Variable variance doesn’t have any outliers.,10076
+diabetes_histograms_numeric.png,Variable Age doesn’t have any outliers.,10991
+Breast_Cancer_boxplots.png,Variable smoothness_se doesn’t have any outliers.,12528
+vehicle_boxplots.png,Variable ELONGATEDNESS doesn’t have any outliers.,7024
+adult_histograms_numeric.png,Variable fnlwgt doesn’t have any outliers.,1523
+Breast_Cancer_boxplots.png,Variable perimeter_se doesn’t have any outliers.,12526
+heart_boxplots.png,Variable age doesn’t have any outliers.,3847
+vehicle_histograms_numeric.png,Variable SCATTER RATIO doesn’t have any outliers.,7023
+Wine_histograms_numeric.png,Variable Alcohol doesn’t have any outliers.,9409
+vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY doesn’t have any outliers.,7019
+Churn_Modelling_boxplots.png,Variable Tenure presents some outliers.,2444
+vehicle_boxplots.png,Variable RADIUS RATIO presents some outliers.,7001
+diabetes_boxplots.png,Variable Insulin presents some outliers.,10980
+Breast_Cancer_histograms_numeric.png,Variable smoothness_se presents some outliers.,12518
+Breast_Cancer_histograms_numeric.png,Variable symmetry_se presents some outliers.,12519
+adult_histograms_numeric.png,Variable age presents some outliers.,1516
+diabetes_boxplots.png,Variable Pregnancies presents some outliers.,10976
+Wine_boxplots.png,Variable Hue presents some outliers.,9406
+diabetes_histograms_numeric.png,Variable DiabetesPedigreeFunction presents some outliers.,10982
+Wine_histograms_numeric.png,Variable Color intensity presents some outliers.,9405
+vehicle_boxplots.png,Variable target presents some outliers.,7016
+Churn_Modelling_boxplots.png,Variable Age presents some outliers.,2443
+vehicle_boxplots.png,Variable ELONGATEDNESS presents some outliers.,7005
+adult_boxplots.png,Variable fnlwgt presents some outliers.,1517
+Iris_histograms_numeric.png,Variable PetalLengthCm presents some outliers.,7945
+vehicle_boxplots.png,Variable MAX LENGTH ASPECT RATIO presents some outliers.,7003
+Breast_Cancer_histograms_numeric.png,Variable radius_worst presents some outliers.,12520
+vehicle_histograms_numeric.png,Variable GYRATIONRADIUS presents some outliers.,7010
+Churn_Modelling_boxplots.png,Variable EstimatedSalary presents some outliers.,2447
+vehicle_histograms_numeric.png,Variable MINORSKEWNESS presents some outliers.,7012
+Wine_histograms_numeric.png,Variable Proanthocyanins presents some outliers.,9404
+Wine_histograms_numeric.png,Variable Alcohol presents some outliers.,9397
+heart_boxplots.png,Variable restecg presents some outliers.,3841
+adult_boxplots.png,Variable hours-per-week presents some outliers.,1521
+Breast_Cancer_boxplots.png,Variable perimeter_worst presents some outliers.,12522
+Iris_boxplots.png,Variable SepalWidthCm presents some outliers.,7944
+vehicle_histograms_numeric.png,Variable COMPACTNESS presents some outliers.,6998
+Iris_histograms_numeric.png,Variable PetalWidthCm presents some outliers.,7946
+diabetes_boxplots.png,Variable SkinThickness presents some outliers.,10979
+heart_boxplots.png,Variable thalach presents some outliers.,3842
+heart_boxplots.png,Variable ca presents some outliers.,3845
+diabetes_histograms_numeric.png,Variable Age presents some outliers.,10983
+heart_boxplots.png,Variable cp presents some outliers.,3838
+heart_boxplots.png,Variable chol presents some outliers.,3840
+vehicle_boxplots.png,Variable HOLLOWS RATIO presents some outliers.,7015
+vehicle_boxplots.png,Variable PR AXISRECTANGULAR presents some outliers.,7006
+adult_boxplots.png,Variable capital-gain presents some outliers.,1519
+BankNoteAuthentication_histograms_numeric.png,Variable curtosis presents some outliers.,10074
+vehicle_boxplots.png,Variable LENGTHRECTANGULAR presents some outliers.,7007
+heart_boxplots.png,Variable trestbps presents some outliers.,3839
+adult_boxplots.png,Variable educational-num presents some outliers.,1518
+Wine_boxplots.png,Variable Ash presents some outliers.,9399
+Breast_Cancer_boxplots.png,Variable texture_worst presents some outliers.,12521
+vehicle_histograms_numeric.png,Variable PR AXIS ASPECT RATIO presents some outliers.,7002
+BankNoteAuthentication_boxplots.png,Variable entropy presents some outliers.,10075
+Breast_Cancer_boxplots.png,Variable perimeter_mean presents some outliers.,12514
+Breast_Cancer_boxplots.png,Variable texture_se presents some outliers.,12515
+Wine_boxplots.png,Variable Alcalinity of ash presents some outliers.,9400
+Wine_histograms_numeric.png,Variable Flavanoids presents some outliers.,9402
+vehicle_histograms_numeric.png,Variable MINORKURTOSIS presents some outliers.,7013
+vehicle_histograms_numeric.png,Variable MAJORKURTOSIS presents some outliers.,7014
+Breast_Cancer_boxplots.png,Variable perimeter_se presents some outliers.,12516
+heart_histograms_numeric.png,Variable oldpeak presents some outliers.,3843
+Churn_Modelling_boxplots.png,Variable NumOfProducts presents some outliers.,2446
+BankNoteAuthentication_histograms_numeric.png,Variable skewness presents some outliers.,10073
+Breast_Cancer_boxplots.png,Variable area_se presents some outliers.,12517
+vehicle_histograms_numeric.png,Variable DISTANCE CIRCULARITY presents some outliers.,7000
+Wine_histograms_numeric.png,Variable Nonflavanoid phenols presents some outliers.,9403
+vehicle_boxplots.png,Variable MAJORSKEWNESS presents some outliers.,7011
+heart_boxplots.png,Variable thal presents some outliers.,3846
+diabetes_boxplots.png,Variable BMI presents some outliers.,10981
+diabetes_histograms_numeric.png,Variable BloodPressure presents some outliers.,10978
+Iris_boxplots.png,Variable SepalLengthCm presents some outliers.,7943
+Wine_boxplots.png,Variable Malic acid presents some outliers.,9398
+Churn_Modelling_boxplots.png,Variable CreditScore presents some outliers.,2442
+Churn_Modelling_boxplots.png,Variable Balance presents some outliers.,2445
+vehicle_histograms_numeric.png,Variable SCATTER RATIO presents some outliers.,7004
+BankNoteAuthentication_histograms_numeric.png,Variable variance presents some outliers.,10072
+vehicle_histograms_numeric.png,Variable MAJORVARIANCE presents some outliers.,7008
+vehicle_histograms_numeric.png,Variable MINORVARIANCE presents some outliers.,7009
+heart_boxplots.png,Variable slope presents some outliers.,3844
+Wine_boxplots.png,Variable OD280-OD315 of diluted wines presents some outliers.,9407
+Wine_boxplots.png,Variable Total phenols presents some outliers.,9401
+adult_histograms_numeric.png,Variable capital-loss presents some outliers.,1520
+diabetes_boxplots.png,Variable Glucose presents some outliers.,10977
+Wine_boxplots.png,Variable Class presents some outliers.,9396
+Breast_Cancer_boxplots.png,Variable texture_mean presents some outliers.,12513
+vehicle_boxplots.png,Variable CIRCULARITY presents some outliers.,6999
+heart_boxplots.png,Variable age presents some outliers.,3837
+Wine_boxplots.png,At least 60 of the variables present outliers.,9393
+BankNoteAuthentication_boxplots.png,At least 60 of the variables present outliers.,10069
+vehicle_boxplots.png,At least 50 of the variables present outliers.,6994
+Breast_Cancer_boxplots.png,At least 50 of the variables present outliers.,12509
+vehicle_histograms_numeric.png,At least 85 of the variables present outliers.,6997
+Churn_Modelling_histograms_numeric.png,At least 75 of the variables present outliers.,2440
+diabetes_boxplots.png,At least 60 of the variables present outliers.,10973
+Iris_histograms_numeric.png,At least 60 of the variables present outliers.,7940
+vehicle_histograms_numeric.png,At least 60 of the variables present outliers.,6995
+heart_boxplots.png,At least 50 of the variables present outliers.,3833
+Churn_Modelling_boxplots.png,At least 50 of the variables present outliers.,2438
+Breast_Cancer_boxplots.png,At least 85 of the variables present outliers.,12512
+vehicle_histograms_numeric.png,At least 75 of the variables present outliers.,6996
+heart_boxplots.png,At least 75 of the variables present outliers.,3835
+adult_histograms_numeric.png,At least 85 of the variables present outliers.,1515
+diabetes_histograms_numeric.png,At least 50 of the variables present outliers.,10972
+diabetes_histograms_numeric.png,At least 85 of the variables present outliers.,10975
+Wine_histograms_numeric.png,At least 50 of the variables present outliers.,9392
+Iris_histograms_numeric.png,At least 50 of the variables present outliers.,7939
+adult_boxplots.png,At least 60 of the variables present outliers.,1513
+heart_histograms_numeric.png,At least 60 of the variables present outliers.,3834
+BankNoteAuthentication_histograms_numeric.png,At least 50 of the variables present outliers.,10068
+adult_boxplots.png,At least 75 of the variables present outliers.,1514
+heart_histograms_numeric.png,At least 85 of the variables present outliers.,3836
+adult_histograms_numeric.png,At least 50 of the variables present outliers.,1512
+Churn_Modelling_boxplots.png,At least 60 of the variables present outliers.,2439
+Iris_histograms_numeric.png,At least 85 of the variables present outliers.,7942
+diabetes_boxplots.png,At least 75 of the variables present outliers.,10974
+Churn_Modelling_boxplots.png,At least 85 of the variables present outliers.,2441
+Wine_boxplots.png,At least 85 of the variables present outliers.,9395
+Wine_boxplots.png,At least 75 of the variables present outliers.,9394
+Breast_Cancer_histograms_numeric.png,At least 75 of the variables present outliers.,12511
+BankNoteAuthentication_histograms_numeric.png,At least 85 of the variables present outliers.,10071
+Breast_Cancer_histograms_numeric.png,At least 60 of the variables present outliers.,12510
+Iris_boxplots.png,At least 75 of the variables present outliers.,7941
+BankNoteAuthentication_boxplots.png,At least 75 of the variables present outliers.,10070
+Churn_Modelling_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,2436
+adult_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,1511
+Iris_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,7937
+vehicle_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,6992
+Breast_Cancer_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,12507
+BankNoteAuthentication_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,10067
+adult_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,1510
+Iris_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,7938
+Breast_Cancer_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,12508
+diabetes_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,10971
+vehicle_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,6993
+heart_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,3831
+heart_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,3832
+diabetes_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,10970
+BankNoteAuthentication_boxplots.png,The boxplots presented show a large number of outliers for most of the numeric variables.,10066
+Churn_Modelling_boxplots.png,The histograms presented show a large number of outliers for most of the numeric variables.,2437
+Wine_histograms_numeric.png,The boxplots presented show a large number of outliers for most of the numeric variables.,9390
+Wine_histograms_numeric.png,The histograms presented show a large number of outliers for most of the numeric variables.,9391
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or texture_worst can be discarded without losing information.,12496
+Iris_correlation_heatmap.png,One of the variables SepalLengthCm or SepalWidthCm can be discarded without losing information.,7927
+Breast_Cancer_correlation_heatmap.png,One of the variables area_se or perimeter_se can be discarded without losing information.,12446
+diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or Insulin can be discarded without losing information.,10946
+vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or LENGTHRECTANGULAR can be discarded without losing information.,6822
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or PR AXISRECTANGULAR can be discarded without losing information.,6809
+vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or CIRCULARITY can be discarded without losing information.,6677
+vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or ELONGATEDNESS can be discarded without losing information.,6782
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MINORSKEWNESS can be discarded without losing information.,6907
+heart_correlation_heatmap.png,One of the variables age or oldpeak can be discarded without losing information.,3804
+Iris_correlation_heatmap.png,One of the variables SepalWidthCm or SepalLengthCm can be discarded without losing information.,7924
+adult_correlation_heatmap.png,One of the variables educational-num or hours-per-week can be discarded without losing information.,1506
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or RADIUS RATIO can be discarded without losing information.,6717
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or LENGTHRECTANGULAR can be discarded without losing information.,6817
+vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6685
+diabetes_correlation_heatmap.png,One of the variables BloodPressure or BMI can be discarded without losing information.,10950
+heart_correlation_heatmap.png,One of the variables cp or restecg can be discarded without losing information.,3787
+vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or LENGTHRECTANGULAR can be discarded without losing information.,6818
+Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Color intensity can be discarded without losing information.,9363
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or RADIUS RATIO can be discarded without losing information.,6718
+Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or radius_worst can be discarded without losing information.,12484
+Iris_correlation_heatmap.png,One of the variables SepalWidthCm or PetalLengthCm can be discarded without losing information.,7931
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or RADIUS RATIO can be discarded without losing information.,6711
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MINORSKEWNESS can be discarded without losing information.,6917
+heart_correlation_heatmap.png,One of the variables trestbps or cp can be discarded without losing information.,3760
+Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Class can be discarded without losing information.,9263
+heart_correlation_heatmap.png,One of the variables restecg or chol can be discarded without losing information.,3780
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MINORSKEWNESS can be discarded without losing information.,6906
+vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or PR AXIS ASPECT RATIO can be discarded without losing information.,6722
+Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Alcohol can be discarded without losing information.,9275
+vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or PR AXISRECTANGULAR can be discarded without losing information.,6793
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or PR AXISRECTANGULAR can be discarded without losing information.,6808
+Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Color intensity can be discarded without losing information.,9364
+vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MAJORSKEWNESS can be discarded without losing information.,6895
+vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MINORVARIANCE can be discarded without losing information.,6849
+vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or CIRCULARITY can be discarded without losing information.,6678
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or target can be discarded without losing information.,6989
+vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MAJORSKEWNESS can be discarded without losing information.,6891
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or target can be discarded without losing information.,6987
+Wine_correlation_heatmap.png,One of the variables Ash or Total phenols can be discarded without losing information.,9315
+adult_correlation_heatmap.png,One of the variables capital-loss or educational-num can be discarded without losing information.,1492
+vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or GYRATIONRADIUS can be discarded without losing information.,6866
+vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or PR AXIS ASPECT RATIO can be discarded without losing information.,6733
+diabetes_correlation_heatmap.png,One of the variables SkinThickness or BloodPressure can be discarded without losing information.,10929
+vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MINORVARIANCE can be discarded without losing information.,6850
+diabetes_correlation_heatmap.png,One of the variables Age or BloodPressure can be discarded without losing information.,10933
+vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or COMPACTNESS can be discarded without losing information.,6662
+Churn_Modelling_correlation_heatmap.png,One of the variables EstimatedSalary or Tenure can be discarded without losing information.,2419
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or texture_mean can be discarded without losing information.,12416
+diabetes_correlation_heatmap.png,One of the variables Glucose or DiabetesPedigreeFunction can be discarded without losing information.,10956
+Wine_correlation_heatmap.png,One of the variables Hue or Nonflavanoid phenols can be discarded without losing information.,9343
+Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or texture_worst can be discarded without losing information.,12495
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or CIRCULARITY can be discarded without losing information.,6671
+vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or RADIUS RATIO can be discarded without losing information.,6706
+heart_correlation_heatmap.png,One of the variables thalach or trestbps can be discarded without losing information.,3772
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or target can be discarded without losing information.,6979
+Wine_correlation_heatmap.png,One of the variables Ash or OD280-OD315 of diluted wines can be discarded without losing information.,9381
+Churn_Modelling_correlation_heatmap.png,One of the variables Age or CreditScore can be discarded without losing information.,2405
+Wine_correlation_heatmap.png,One of the variables Total phenols or Malic acid can be discarded without losing information.,9283
+vehicle_correlation_heatmap.png,One of the variables DISTANCE CIRCULARITY or MAJORSKEWNESS can be discarded without losing information.,6885
+diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or BMI can be discarded without losing information.,10953
+vehicle_correlation_heatmap.png,One of the variables target or MINORKURTOSIS can be discarded without losing information.,6936
+vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MAJORKURTOSIS can be discarded without losing information.,6949
+vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6697
+Wine_correlation_heatmap.png,One of the variables Alcohol or Hue can be discarded without losing information.,9368
+vehicle_correlation_heatmap.png,One of the variables target or PR AXISRECTANGULAR can be discarded without losing information.,6810
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or texture_se can be discarded without losing information.,12441
+vehicle_correlation_heatmap.png,One of the variables target or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6756
+vehicle_correlation_heatmap.png,One of the variables target or ELONGATEDNESS can be discarded without losing information.,6792
+Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Alcalinity of ash can be discarded without losing information.,9311
+vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or GYRATIONRADIUS can be discarded without losing information.,6876
+heart_correlation_heatmap.png,One of the variables trestbps or slope can be discarded without losing information.,3815
+Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Total phenols can be discarded without losing information.,9318
+Wine_correlation_heatmap.png,One of the variables Hue or Alcalinity of ash can be discarded without losing information.,9310
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or COMPACTNESS can be discarded without losing information.,6663
+heart_correlation_heatmap.png,One of the variables cp or trestbps can be discarded without losing information.,3769
+Wine_correlation_heatmap.png,One of the variables Color intensity or Flavanoids can be discarded without losing information.,9331
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MAJORVARIANCE can be discarded without losing information.,6842
+vehicle_correlation_heatmap.png,One of the variables target or MINORVARIANCE can be discarded without losing information.,6864
+Wine_correlation_heatmap.png,One of the variables Ash or Flavanoids can be discarded without losing information.,9326
+vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or COMPACTNESS can be discarded without losing information.,6661
+Iris_correlation_heatmap.png,One of the variables SepalWidthCm or PetalWidthCm can be discarded without losing information.,7934
+vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6724
+Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Total phenols can be discarded without losing information.,9322
+heart_correlation_heatmap.png,One of the variables restecg or oldpeak can be discarded without losing information.,3808
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or PR AXISRECTANGULAR can be discarded without losing information.,6798
+Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or radius_worst can be discarded without losing information.,12485
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or perimeter_se can be discarded without losing information.,12451
+adult_correlation_heatmap.png,One of the variables age or fnlwgt can be discarded without losing information.,1484
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or COMPACTNESS can be discarded without losing information.,6666
+vehicle_correlation_heatmap.png,One of the variables target or LENGTHRECTANGULAR can be discarded without losing information.,6828
+adult_correlation_heatmap.png,One of the variables educational-num or age can be discarded without losing information.,1480
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MAJORKURTOSIS can be discarded without losing information.,6942
+Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Flavanoids can be discarded without losing information.,9333
+vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MAJORKURTOSIS can be discarded without losing information.,6940
+adult_correlation_heatmap.png,One of the variables fnlwgt or capital-gain can be discarded without losing information.,1495
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or area_se can be discarded without losing information.,12453
+Wine_correlation_heatmap.png,One of the variables Malic acid or Hue can be discarded without losing information.,9369
+adult_correlation_heatmap.png,One of the variables age or capital-loss can be discarded without losing information.,1499
+Churn_Modelling_correlation_heatmap.png,One of the variables Tenure or NumOfProducts can be discarded without losing information.,2427
+vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MINORVARIANCE can be discarded without losing information.,6857
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or perimeter_worst can be discarded without losing information.,12505
+Wine_correlation_heatmap.png,One of the variables Class or Proanthocyanins can be discarded without losing information.,9345
+heart_correlation_heatmap.png,One of the variables trestbps or restecg can be discarded without losing information.,3788
+Wine_correlation_heatmap.png,One of the variables Ash or Malic acid can be discarded without losing information.,9281
+vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or COMPACTNESS can be discarded without losing information.,6653
+BankNoteAuthentication_correlation_heatmap.png,One of the variables skewness or variance can be discarded without losing information.,10053
+vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6749
+diabetes_correlation_heatmap.png,One of the variables Age or Pregnancies can be discarded without losing information.,10919
+Wine_correlation_heatmap.png,One of the variables Total phenols or Nonflavanoid phenols can be discarded without losing information.,9339
+Wine_correlation_heatmap.png,One of the variables Color intensity or Proanthocyanins can be discarded without losing information.,9353
+vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6691
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_mean or perimeter_mean can be discarded without losing information.,12425
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MINORSKEWNESS can be discarded without losing information.,6915
+Wine_correlation_heatmap.png,One of the variables Total phenols or OD280-OD315 of diluted wines can be discarded without losing information.,9383
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or HOLLOWS RATIO can be discarded without losing information.,6971
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or perimeter_worst can be discarded without losing information.,12498
+vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or ELONGATEDNESS can be discarded without losing information.,6787
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MAJORSKEWNESS can be discarded without losing information.,6897
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or COMPACTNESS can be discarded without losing information.,6655
+heart_correlation_heatmap.png,One of the variables oldpeak or chol can be discarded without losing information.,3782
+Wine_correlation_heatmap.png,One of the variables Flavanoids or Alcohol can be discarded without losing information.,9273
+vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or PR AXIS ASPECT RATIO can be discarded without losing information.,6731
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or RADIUS RATIO can be discarded without losing information.,6719
+vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or RADIUS RATIO can be discarded without losing information.,6703
+Wine_correlation_heatmap.png,One of the variables Color intensity or Hue can be discarded without losing information.,9376
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or SCATTER RATIO can be discarded without losing information.,6765
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or target can be discarded without losing information.,6990
+Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Flavanoids can be discarded without losing information.,9327
+Wine_correlation_heatmap.png,One of the variables Total phenols or Class can be discarded without losing information.,9261
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or HOLLOWS RATIO can be discarded without losing information.,6964
+adult_correlation_heatmap.png,One of the variables capital-gain or educational-num can be discarded without losing information.,1491
+diabetes_correlation_heatmap.png,One of the variables BMI or Pregnancies can be discarded without losing information.,10917
+heart_correlation_heatmap.png,One of the variables chol or slope can be discarded without losing information.,3816
+Wine_correlation_heatmap.png,One of the variables Alcohol or Malic acid can be discarded without losing information.,9280
+Wine_correlation_heatmap.png,One of the variables Ash or Proanthocyanins can be discarded without losing information.,9348
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MAJORSKEWNESS can be discarded without losing information.,6892
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or smoothness_se can be discarded without losing information.,12462
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or area_se can be discarded without losing information.,12460
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6753
+Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Nonflavanoid phenols can be discarded without losing information.,9344
+Wine_correlation_heatmap.png,One of the variables Proanthocyanins or OD280-OD315 of diluted wines can be discarded without losing information.,9386
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or texture_mean can be discarded without losing information.,12423
+diabetes_correlation_heatmap.png,One of the variables BMI or Age can be discarded without losing information.,10967
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or PR AXIS ASPECT RATIO can be discarded without losing information.,6735
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or COMPACTNESS can be discarded without losing information.,6665
+Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Total phenols can be discarded without losing information.,9319
+Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Malic acid can be discarded without losing information.,9286
+vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or LENGTHRECTANGULAR can be discarded without losing information.,6814
+Wine_correlation_heatmap.png,One of the variables Alcohol or Color intensity can be discarded without losing information.,9357
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or ELONGATEDNESS can be discarded without losing information.,6791
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or CIRCULARITY can be discarded without losing information.,6675
+vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or MINORSKEWNESS can be discarded without losing information.,6909
+vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MINORVARIANCE can be discarded without losing information.,6847
+Wine_correlation_heatmap.png,One of the variables Flavanoids or Total phenols can be discarded without losing information.,9317
+heart_correlation_heatmap.png,One of the variables oldpeak or age can be discarded without losing information.,3755
+diabetes_correlation_heatmap.png,One of the variables Glucose or SkinThickness can be discarded without losing information.,10935
+Breast_Cancer_correlation_heatmap.png,One of the variables symmetry_se or texture_worst can be discarded without losing information.,12494
+heart_correlation_heatmap.png,One of the variables age or slope can be discarded without losing information.,3813
+heart_correlation_heatmap.png,One of the variables chol or oldpeak can be discarded without losing information.,3807
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MAJORSKEWNESS can be discarded without losing information.,6896
+Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Proanthocyanins can be discarded without losing information.,9352
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or perimeter_worst can be discarded without losing information.,12500
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or area_se can be discarded without losing information.,12455
+diabetes_correlation_heatmap.png,One of the variables Pregnancies or BMI can be discarded without losing information.,10948
+Wine_correlation_heatmap.png,One of the variables Proanthocyanins or Nonflavanoid phenols can be discarded without losing information.,9341
+vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or ELONGATEDNESS can be discarded without losing information.,6778
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or GYRATIONRADIUS can be discarded without losing information.,6870
+heart_correlation_heatmap.png,One of the variables age or cp can be discarded without losing information.,3759
+vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or target can be discarded without losing information.,6976
+Wine_correlation_heatmap.png,One of the variables Malic acid or Class can be discarded without losing information.,9258
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MAJORKURTOSIS can be discarded without losing information.,6946
+heart_correlation_heatmap.png,One of the variables thalach or slope can be discarded without losing information.,3818
+diabetes_correlation_heatmap.png,One of the variables SkinThickness or BMI can be discarded without losing information.,10951
+diabetes_correlation_heatmap.png,One of the variables SkinThickness or Insulin can be discarded without losing information.,10944
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or LENGTHRECTANGULAR can be discarded without losing information.,6824
+Churn_Modelling_correlation_heatmap.png,One of the variables Tenure or Balance can be discarded without losing information.,2422
+vehicle_correlation_heatmap.png,One of the variables CIRCULARITY or RADIUS RATIO can be discarded without losing information.,6704
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or MAX LENGTH ASPECT RATIO can be discarded without losing information.,6747
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or MINORKURTOSIS can be discarded without losing information.,6924
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or CIRCULARITY can be discarded without losing information.,6681
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or LENGTHRECTANGULAR can be discarded without losing information.,6826
+Wine_correlation_heatmap.png,One of the variables Color intensity or Class can be discarded without losing information.,9265
+heart_correlation_heatmap.png,One of the variables restecg or thal can be discarded without losing information.,3826
+vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or RADIUS RATIO can be discarded without losing information.,6713
+heart_correlation_heatmap.png,One of the variables restecg or age can be discarded without losing information.,3753
+heart_correlation_heatmap.png,One of the variables trestbps or thal can be discarded without losing information.,3824
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or symmetry_se can be discarded without losing information.,12472
+Churn_Modelling_correlation_heatmap.png,One of the variables CreditScore or Tenure can be discarded without losing information.,2415
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or perimeter_mean can be discarded without losing information.,12427
+diabetes_correlation_heatmap.png,One of the variables BloodPressure or SkinThickness can be discarded without losing information.,10936
+Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or perimeter_se can be discarded without losing information.,12447
+vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or CIRCULARITY can be discarded without losing information.,6670
+Wine_correlation_heatmap.png,One of the variables Hue or Ash can be discarded without losing information.,9299
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or RADIUS RATIO can be discarded without losing information.,6708
+Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Color intensity can be discarded without losing information.,9366
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or SCATTER RATIO can be discarded without losing information.,6762
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or texture_mean can be discarded without losing information.,12417
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or smoothness_se can be discarded without losing information.,12468
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or PR AXISRECTANGULAR can be discarded without losing information.,6807
+vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or HOLLOWS RATIO can be discarded without losing information.,6955
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or perimeter_mean can be discarded without losing information.,12433
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or texture_se can be discarded without losing information.,12435
+vehicle_correlation_heatmap.png,One of the variables LENGTHRECTANGULAR or DISTANCE CIRCULARITY can be discarded without losing information.,6693
+Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Alcohol can be discarded without losing information.,9274
+heart_correlation_heatmap.png,One of the variables cp or thalach can be discarded without losing information.,3796
+diabetes_correlation_heatmap.png,One of the variables Glucose or Age can be discarded without losing information.,10963
+Wine_correlation_heatmap.png,One of the variables Class or Hue can be discarded without losing information.,9367
+Wine_correlation_heatmap.png,One of the variables Flavanoids or Nonflavanoid phenols can be discarded without losing information.,9340
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or CIRCULARITY can be discarded without losing information.,6683
+vehicle_correlation_heatmap.png,One of the variables target or CIRCULARITY can be discarded without losing information.,6684
+Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or perimeter_worst can be discarded without losing information.,12504
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6737
+vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or GYRATIONRADIUS can be discarded without losing information.,6869
+Wine_correlation_heatmap.png,One of the variables Hue or Class can be discarded without losing information.,9266
+vehicle_correlation_heatmap.png,One of the variables RADIUS RATIO or MAJORSKEWNESS can be discarded without losing information.,6886
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or MINORKURTOSIS can be discarded without losing information.,6933
+Wine_correlation_heatmap.png,One of the variables Alcalinity of ash or Ash can be discarded without losing information.,9293
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or MINORVARIANCE can be discarded without losing information.,6853
+Breast_Cancer_correlation_heatmap.png,One of the variables radius_worst or texture_se can be discarded without losing information.,12440
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or GYRATIONRADIUS can be discarded without losing information.,6878
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or MAJORVARIANCE can be discarded without losing information.,6845
+diabetes_correlation_heatmap.png,One of the variables DiabetesPedigreeFunction or Pregnancies can be discarded without losing information.,10918
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_worst or area_se can be discarded without losing information.,12459
+Wine_correlation_heatmap.png,One of the variables Color intensity or Nonflavanoid phenols can be discarded without losing information.,9342
+vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or target can be discarded without losing information.,6984
+Breast_Cancer_correlation_heatmap.png,One of the variables smoothness_se or symmetry_se can be discarded without losing information.,12475
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or radius_worst can be discarded without losing information.,12480
+vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or MINORKURTOSIS can be discarded without losing information.,6929
+BankNoteAuthentication_correlation_heatmap.png,One of the variables curtosis or variance can be discarded without losing information.,10054
+heart_correlation_heatmap.png,One of the variables restecg or trestbps can be discarded without losing information.,3771
+vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or HOLLOWS RATIO can be discarded without losing information.,6963
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MAJORSKEWNESS can be discarded without losing information.,6898
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or MAJORVARIANCE can be discarded without losing information.,6844
+vehicle_correlation_heatmap.png,One of the variables MINORSKEWNESS or DISTANCE CIRCULARITY can be discarded without losing information.,6698
+vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or MINORVARIANCE can be discarded without losing information.,6858
+vehicle_correlation_heatmap.png,One of the variables PR AXIS ASPECT RATIO or target can be discarded without losing information.,6977
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_mean or texture_worst can be discarded without losing information.,12489
+adult_correlation_heatmap.png,One of the variables educational-num or fnlwgt can be discarded without losing information.,1485
+vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or GYRATIONRADIUS can be discarded without losing information.,6877
+Wine_correlation_heatmap.png,One of the variables OD280-OD315 of diluted wines or Proanthocyanins can be discarded without losing information.,9355
+heart_correlation_heatmap.png,One of the variables slope or thal can be discarded without losing information.,3828
+diabetes_correlation_heatmap.png,One of the variables Age or Insulin can be discarded without losing information.,10947
+heart_correlation_heatmap.png,One of the variables thal or trestbps can be discarded without losing information.,3776
+Churn_Modelling_correlation_heatmap.png,One of the variables Age or Tenure can be discarded without losing information.,2416
+vehicle_correlation_heatmap.png,One of the variables MINORVARIANCE or PR AXISRECTANGULAR can be discarded without losing information.,6803
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6725
+Wine_correlation_heatmap.png,One of the variables Class or Ash can be discarded without losing information.,9290
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_worst or texture_se can be discarded without losing information.,12442
+heart_correlation_heatmap.png,One of the variables ca or thal can be discarded without losing information.,3829
+heart_correlation_heatmap.png,One of the variables thalach or chol can be discarded without losing information.,3781
+Wine_correlation_heatmap.png,One of the variables Hue or Malic acid can be discarded without losing information.,9288
+Wine_correlation_heatmap.png,One of the variables Ash or Nonflavanoid phenols can be discarded without losing information.,9337
+vehicle_correlation_heatmap.png,One of the variables ELONGATEDNESS or MAJORKURTOSIS can be discarded without losing information.,6944
+heart_correlation_heatmap.png,One of the variables age or thal can be discarded without losing information.,3822
+vehicle_correlation_heatmap.png,One of the variables MAJORSKEWNESS or CIRCULARITY can be discarded without losing information.,6679
+vehicle_correlation_heatmap.png,One of the variables HOLLOWS RATIO or GYRATIONRADIUS can be discarded without losing information.,6881
+adult_correlation_heatmap.png,One of the variables capital-loss or capital-gain can be discarded without losing information.,1497
+diabetes_correlation_heatmap.png,One of the variables Pregnancies or BloodPressure can be discarded without losing information.,10927
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or CIRCULARITY can be discarded without losing information.,6672
+Breast_Cancer_correlation_heatmap.png,One of the variables perimeter_se or radius_worst can be discarded without losing information.,12482
+vehicle_correlation_heatmap.png,One of the variables MAJORVARIANCE or HOLLOWS RATIO can be discarded without losing information.,6965
+Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Hue can be discarded without losing information.,9374
+heart_correlation_heatmap.png,One of the variables ca or thalach can be discarded without losing information.,3802
+Wine_correlation_heatmap.png,One of the variables Class or Color intensity can be discarded without losing information.,9356
+diabetes_correlation_heatmap.png,One of the variables Insulin or BloodPressure can be discarded without losing information.,10930
+adult_correlation_heatmap.png,One of the variables age or educational-num can be discarded without losing information.,1489
+Wine_correlation_heatmap.png,One of the variables Ash or Alcalinity of ash can be discarded without losing information.,9304
+Iris_correlation_heatmap.png,One of the variables SepalLengthCm or PetalWidthCm can be discarded without losing information.,7933
+Iris_correlation_heatmap.png,One of the variables PetalWidthCm or SepalLengthCm can be discarded without losing information.,7926
+Wine_correlation_heatmap.png,One of the variables Nonflavanoid phenols or Flavanoids can be discarded without losing information.,9329
+heart_correlation_heatmap.png,One of the variables chol or restecg can be discarded without losing information.,3789
+Iris_correlation_heatmap.png,One of the variables PetalLengthCm or SepalLengthCm can be discarded without losing information.,7925
+heart_correlation_heatmap.png,One of the variables cp or oldpeak can be discarded without losing information.,3805
+Wine_correlation_heatmap.png,One of the variables Alcohol or Nonflavanoid phenols can be discarded without losing information.,9335
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or perimeter_se can be discarded without losing information.,12445
+heart_correlation_heatmap.png,One of the variables trestbps or oldpeak can be discarded without losing information.,3806
+Breast_Cancer_correlation_heatmap.png,One of the variables texture_se or area_se can be discarded without losing information.,12454
+adult_correlation_heatmap.png,One of the variables fnlwgt or capital-loss can be discarded without losing information.,1500
+vehicle_correlation_heatmap.png,One of the variables MAJORKURTOSIS or DISTANCE CIRCULARITY can be discarded without losing information.,6700
+diabetes_correlation_heatmap.png,One of the variables BMI or SkinThickness can be discarded without losing information.,10938
+vehicle_correlation_heatmap.png,One of the variables COMPACTNESS or MINORSKEWNESS can be discarded without losing information.,6901
+vehicle_correlation_heatmap.png,One of the variables GYRATIONRADIUS or HOLLOWS RATIO can be discarded without losing information.,6967
+heart_correlation_heatmap.png,One of the variables slope or oldpeak can be discarded without losing information.,3810
+Breast_Cancer_correlation_heatmap.png,One of the variables area_se or perimeter_mean can be discarded without losing information.,12428
+vehicle_correlation_heatmap.png,One of the variables MAX LENGTH ASPECT RATIO or target can be discarded without losing information.,6978
+vehicle_correlation_heatmap.png,One of the variables SCATTER RATIO or PR AXIS ASPECT RATIO can be discarded without losing information.,6726
+vehicle_correlation_heatmap.png,One of the variables MINORKURTOSIS or HOLLOWS RATIO can be discarded without losing information.,6970
+vehicle_correlation_heatmap.png,One of the variables PR AXISRECTANGULAR or RADIUS RATIO can be discarded without losing information.,6710
+diabetes_correlation_heatmap.png,One of the variables Insulin or DiabetesPedigreeFunction can be discarded without losing information.,10959
+Breast_Cancer_correlation_heatmap.png,The variable perimeter_worst can be discarded without risking losing information.,12415
+heart_correlation_heatmap.png,The variable trestbps can be discarded without risking losing information.,3742
+Wine_correlation_heatmap.png,The variable OD280-OD315 of diluted wines can be discarded without risking losing information.,9256
+vehicle_correlation_heatmap.png,The variable PR AXIS ASPECT RATIO can be discarded without risking losing information.,6635
+diabetes_correlation_heatmap.png,The variable BMI can be discarded without risking losing information.,10910
+Iris_correlation_heatmap.png,The variable PetalWidthCm can be discarded without risking losing information.,7923
+vehicle_correlation_heatmap.png,The variable MAJORKURTOSIS can be discarded without risking losing information.,6647
+vehicle_correlation_heatmap.png,The variable CIRCULARITY can be discarded without risking losing information.,6632
+Iris_correlation_heatmap.png,The variable SepalLengthCm can be discarded without risking losing information.,7920
+adult_correlation_heatmap.png,The variable fnlwgt can be discarded without risking losing information.,1474
+adult_correlation_heatmap.png,The variable age can be discarded without risking losing information.,1473
+BankNoteAuthentication_correlation_heatmap.png,The variable skewness can be discarded without risking losing information.,10050
+diabetes_correlation_heatmap.png,The variable Glucose can be discarded without risking losing information.,10906
+adult_correlation_heatmap.png,The variable capital-loss can be discarded without risking losing information.,1477
+Wine_correlation_heatmap.png,The variable Class can be discarded without risking losing information.,9245
+vehicle_correlation_heatmap.png,The variable COMPACTNESS can be discarded without risking losing information.,6631
+vehicle_correlation_heatmap.png,The variable DISTANCE CIRCULARITY can be discarded without risking losing information.,6633
+Wine_correlation_heatmap.png,The variable Malic acid can be discarded without risking losing information.,9247
+vehicle_correlation_heatmap.png,The variable target can be discarded without risking losing information.,6649
+BankNoteAuthentication_correlation_heatmap.png,The variable curtosis can be discarded without risking losing information.,10051
+vehicle_correlation_heatmap.png,The variable RADIUS RATIO can be discarded without risking losing information.,6634
+heart_correlation_heatmap.png,The variable thalach can be discarded without risking losing information.,3745
+Wine_correlation_heatmap.png,The variable Alcalinity of ash can be discarded without risking losing information.,9249
+Churn_Modelling_correlation_heatmap.png,The variable NumOfProducts can be discarded without risking losing information.,2403
+Breast_Cancer_correlation_heatmap.png,The variable perimeter_mean can be discarded without risking losing information.,12407
+diabetes_correlation_heatmap.png,The variable DiabetesPedigreeFunction can be discarded without risking losing information.,10911
+Wine_correlation_heatmap.png,The variable Proanthocyanins can be discarded without risking losing information.,9253
+Wine_correlation_heatmap.png,The variable Flavanoids can be discarded without risking losing information.,9251
+Breast_Cancer_correlation_heatmap.png,The variable area_se can be discarded without risking losing information.,12410
+vehicle_correlation_heatmap.png,The variable LENGTHRECTANGULAR can be discarded without risking losing information.,6640
+heart_correlation_heatmap.png,The variable chol can be discarded without risking losing information.,3743
+vehicle_correlation_heatmap.png,The variable PR AXISRECTANGULAR can be discarded without risking losing information.,6639
+Churn_Modelling_correlation_heatmap.png,The variable CreditScore can be discarded without risking losing information.,2399
+vehicle_correlation_heatmap.png,The variable ELONGATEDNESS can be discarded without risking losing information.,6638
+Breast_Cancer_correlation_heatmap.png,The variable radius_worst can be discarded without risking losing information.,12413
+BankNoteAuthentication_correlation_heatmap.png,The variable variance can be discarded without risking losing information.,10049
+adult_correlation_heatmap.png,The variable capital-gain can be discarded without risking losing information.,1476
+diabetes_correlation_heatmap.png,The variable BloodPressure can be discarded without risking losing information.,10907
+Iris_correlation_heatmap.png,The variable SepalWidthCm can be discarded without risking losing information.,7921
+BankNoteAuthentication_correlation_heatmap.png,The variable entropy can be discarded without risking losing information.,10052
+Breast_Cancer_correlation_heatmap.png,The variable texture_se can be discarded without risking losing information.,12408
+vehicle_correlation_heatmap.png,The variable MAJORVARIANCE can be discarded without risking losing information.,6641
+Churn_Modelling_correlation_heatmap.png,The variable Tenure can be discarded without risking losing information.,2401
+vehicle_correlation_heatmap.png,The variable MAX LENGTH ASPECT RATIO can be discarded without risking losing information.,6636
+Wine_correlation_heatmap.png,The variable Color intensity can be discarded without risking losing information.,9254
+Churn_Modelling_correlation_heatmap.png,The variable Age can be discarded without risking losing information.,2400
+vehicle_correlation_heatmap.png,The variable SCATTER RATIO can be discarded without risking losing information.,6637
+vehicle_correlation_heatmap.png,The variable MINORKURTOSIS can be discarded without risking losing information.,6646
+Churn_Modelling_correlation_heatmap.png,The variable EstimatedSalary can be discarded without risking losing information.,2404
+vehicle_correlation_heatmap.png,The variable MINORVARIANCE can be discarded without risking losing information.,6642
+Breast_Cancer_correlation_heatmap.png,The variable symmetry_se can be discarded without risking losing information.,12412
+Wine_correlation_heatmap.png,The variable Hue can be discarded without risking losing information.,9255
+heart_correlation_heatmap.png,The variable age can be discarded without risking losing information.,3740
+vehicle_correlation_heatmap.png,The variable MINORSKEWNESS can be discarded without risking losing information.,6645
+adult_correlation_heatmap.png,The variable hours-per-week can be discarded without risking losing information.,1478
+vehicle_correlation_heatmap.png,The variable GYRATIONRADIUS can be discarded without risking losing information.,6643
+vehicle_correlation_heatmap.png,The variable HOLLOWS RATIO can be discarded without risking losing information.,6648
+Wine_correlation_heatmap.png,The variable Ash can be discarded without risking losing information.,9248
+adult_correlation_heatmap.png,The variable educational-num can be discarded without risking losing information.,1475
+Breast_Cancer_correlation_heatmap.png,The variable texture_mean can be discarded without risking losing information.,12406
+vehicle_correlation_heatmap.png,The variable MAJORSKEWNESS can be discarded without risking losing information.,6644
+heart_correlation_heatmap.png,The variable restecg can be discarded without risking losing information.,3744
+diabetes_correlation_heatmap.png,The variable Insulin can be discarded without risking losing information.,10909
+Wine_correlation_heatmap.png,The variable Alcohol can be discarded without risking losing information.,9246
+heart_correlation_heatmap.png,The variable oldpeak can be discarded without risking losing information.,3746
+heart_correlation_heatmap.png,The variable thal can be discarded without risking losing information.,3749
+heart_correlation_heatmap.png,The variable slope can be discarded without risking losing information.,3747
+heart_correlation_heatmap.png,The variable cp can be discarded without risking losing information.,3741
+diabetes_correlation_heatmap.png,The variable Age can be discarded without risking losing information.,10912
+Wine_correlation_heatmap.png,The variable Nonflavanoid phenols can be discarded without risking losing information.,9252
+diabetes_correlation_heatmap.png,The variable SkinThickness can be discarded without risking losing information.,10908
+Wine_correlation_heatmap.png,The variable Total phenols can be discarded without risking losing information.,9250
+Breast_Cancer_correlation_heatmap.png,The variable smoothness_se can be discarded without risking losing information.,12411
+Breast_Cancer_correlation_heatmap.png,The variable perimeter_se can be discarded without risking losing information.,12409
+Iris_correlation_heatmap.png,The variable PetalLengthCm can be discarded without risking losing information.,7922
+Churn_Modelling_correlation_heatmap.png,The variable Balance can be discarded without risking losing information.,2402
+Breast_Cancer_correlation_heatmap.png,The variable texture_worst can be discarded without risking losing information.,12414
+heart_correlation_heatmap.png,The variable ca can be discarded without risking losing information.,3748
+diabetes_correlation_heatmap.png,The variable Pregnancies can be discarded without risking losing information.,10905
+Churn_Modelling_correlation_heatmap.png,"Variables Balance and NumOfProducts are redundant, but we can’t say the same for the pair HasCrCard and IsActiveMember.",2290
+diabetes_correlation_heatmap.png,Variables Glucose and Insulin seem to be useful for classification tasks.,10605
+heart_correlation_heatmap.png,"Variables cp and trestbps are redundant, but we can’t say the same for the pair chol and fbs.",3661
+Wine_correlation_heatmap.png,"Variables Alcohol and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Alcalinity of ash and Hue.",9096
+vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and PR AXIS ASPECT RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and SCATTER RATIO.",6077
+adult_correlation_heatmap.png,"Variables gender and hours-per-week are redundant, but we can’t say the same for the pair educational-num and capital-gain.",1293
+Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Malic acid and Color intensity.",9114
+vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORKURTOSIS seem to be useful for classification tasks.,5387
+Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and perimeter_se are redundant, but we can’t say the same for the pair texture_se and smoothness_se.",12333
+Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Age seem to be useful for classification tasks.,2085
+adult_correlation_heatmap.png,Variables age and fnlwgt seem to be useful for classification tasks.,1027
+vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORSKEWNESS.",6495
+heart_correlation_heatmap.png,"Variables restecg and ca are redundant, but we can’t say the same for the pair sex and cp.",3703
+Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and symmetry_se are redundant, but we can’t say the same for the pair texture_mean and smoothness_se.",12340
+heart_correlation_heatmap.png,"Variables slope and thal are redundant, but we can’t say the same for the pair trestbps and ca.",3686
+Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Balance seem to be useful for classification tasks.,2095
+vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and GYRATIONRADIUS are redundant, but we can’t say the same for the pair CIRCULARITY and MINORSKEWNESS.",6515
+adult_correlation_heatmap.png,"Variables occupation and hours-per-week are redundant, but we can’t say the same for the pair education and gender.",1396
+vehicle_correlation_heatmap.png,Variables CIRCULARITY and MINORSKEWNESS seem to be useful for classification tasks.,5341
+Churn_Modelling_correlation_heatmap.png,"Variables Geography and NumOfProducts are redundant, but we can’t say the same for the pair Gender and Tenure.",2291
+Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair Tenure and NumOfProducts.",2344
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORKURTOSIS.",6595
+vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and DISTANCE CIRCULARITY are redundant.,5720
+vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR.",6041
+BankNoteAuthentication_correlation_heatmap.png,Variables variance and curtosis seem to be useful for classification tasks.,9972
+vehicle_correlation_heatmap.png,Variables SCATTER RATIO and MAJORVARIANCE are redundant.,5863
+vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MINORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and PR AXIS ASPECT RATIO.",6316
+Wine_correlation_heatmap.png,"Variables Ash and Total phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Flavanoids.",9193
+diabetes_correlation_heatmap.png,Variables Insulin and Pregnancies seem to be useful for classification tasks.,10579
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and HOLLOWS RATIO.",6500
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORSKEWNESS.",6576
+diabetes_correlation_heatmap.png,"Variables Pregnancies and Glucose are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10880
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6431
+vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and RADIUS RATIO are redundant.,5745
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORKURTOSIS.",6363
+Breast_Cancer_correlation_heatmap.png,"Variables perimeter_mean and perimeter_se are redundant, but we can’t say the same for the pair texture_se and symmetry_se.",12314
+vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and PR AXISRECTANGULAR seem to be useful for classification tasks.,5240
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and HOLLOWS RATIO are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and GYRATIONRADIUS.",6315
+vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and GYRATIONRADIUS.",6062
+vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and LENGTHRECTANGULAR are redundant.,5843
+Breast_Cancer_correlation_heatmap.png,Variables perimeter_se and texture_worst seem to be useful for classification tasks.,11990
+vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and target seem to be useful for classification tasks.,5422
+Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and texture_worst are redundant, but we can’t say the same for the pair texture_se and radius_worst.",12271
+heart_correlation_heatmap.png,Variables age and restecg seem to be useful for classification tasks.,3164
+heart_correlation_heatmap.png,"Variables age and thal are redundant, but we can’t say the same for the pair sex and exang.",3482
+adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1405
+diabetes_correlation_heatmap.png,Variables Insulin and BMI seem to be useful for classification tasks.,10615
+heart_correlation_heatmap.png,"Variables oldpeak and slope are redundant, but we can’t say the same for the pair fbs and exang.",3446
+Wine_correlation_heatmap.png,Variables Color intensity and Nonflavanoid phenols seem to be useful for classification tasks.,8689
+diabetes_correlation_heatmap.png,"Variables Pregnancies and BloodPressure are redundant, but we can’t say the same for the pair BMI and Age.",10869
+vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE are redundant, but we can’t say the same for the pair MINORSKEWNESS and MAJORKURTOSIS.",6496
+heart_correlation_heatmap.png,"Variables sex and slope are redundant, but we can’t say the same for the pair age and thal.",3508
+diabetes_correlation_heatmap.png,"Variables BMI and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair SkinThickness and Insulin.",10860
+Wine_correlation_heatmap.png,Variables Class and Total phenols are redundant.,8948
+Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9150
+Wine_correlation_heatmap.png,"Variables Total phenols and Color intensity are redundant, but we can’t say the same for the pair Ash and Alcalinity of ash.",9228
+diabetes_correlation_heatmap.png,"Variables BloodPressure and Insulin are redundant, but we can’t say the same for the pair SkinThickness and Age.",10844
+vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair CIRCULARITY and RADIUS RATIO.",6390
+Wine_correlation_heatmap.png,"Variables Malic acid and Ash are redundant, but we can’t say the same for the pair Alcalinity of ash and Flavanoids.",9095
+Breast_Cancer_correlation_heatmap.png,Variables texture_mean and perimeter_worst are redundant.,12217
+vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5135
+vehicle_correlation_heatmap.png,"Variables CIRCULARITY and ELONGATEDNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and HOLLOWS RATIO.",6297
+vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORSKEWNESS are redundant.,5919
+vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5173
+Churn_Modelling_correlation_heatmap.png,"Variables Gender and Balance are redundant, but we can’t say the same for the pair NumOfProducts and HasCrCard.",2326
+Breast_Cancer_correlation_heatmap.png,"Variables area_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and smoothness_se.",12358
+heart_correlation_heatmap.png,Variables cp and trestbps are redundant.,3358
+Churn_Modelling_correlation_heatmap.png,"Variables Balance and IsActiveMember are redundant, but we can’t say the same for the pair CreditScore and Geography.",2279
+Churn_Modelling_correlation_heatmap.png,Variables Balance and Age are redundant.,2196
+Wine_correlation_heatmap.png,"Variables Malic acid and Hue are redundant, but we can’t say the same for the pair Alcalinity of ash and Color intensity.",9072
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and GYRATIONRADIUS.",6326
+heart_correlation_heatmap.png,Variables chol and cp seem to be useful for classification tasks.,3139
+vehicle_correlation_heatmap.png,Variables MINORVARIANCE and MAJORSKEWNESS are redundant.,5922
+vehicle_correlation_heatmap.png,"Variables PR AXISRECTANGULAR and HOLLOWS RATIO are redundant, but we can’t say the same for the pair CIRCULARITY and MAJORVARIANCE.",6052
+Wine_correlation_heatmap.png,Variables Malic acid and Nonflavanoid phenols are redundant.,8972
+vehicle_correlation_heatmap.png,"Variables MINORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORSKEWNESS.",6479
+vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORKURTOSIS.",6030
+Breast_Cancer_correlation_heatmap.png,Variables area_se and perimeter_se are redundant.,12166
+heart_correlation_heatmap.png,"Variables restecg and exang are redundant, but we can’t say the same for the pair fbs and thalach.",3657
+Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Age are redundant.,2197
+Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Class seem to be useful for classification tasks.,8614
+heart_correlation_heatmap.png,"Variables chol and fbs are redundant, but we can’t say the same for the pair cp and ca.",3470
+adult_correlation_heatmap.png,"Variables education and gender are redundant, but we can’t say the same for the pair educational-num and relationship.",1379
+adult_correlation_heatmap.png,"Variables capital-gain and capital-loss are redundant, but we can’t say the same for the pair workclass and relationship.",1317
+vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and MAJORKURTOSIS.",6022
+diabetes_correlation_heatmap.png,Variables Age and Insulin seem to be useful for classification tasks.,10610
+Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_mean are redundant.,12150
+Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and perimeter_worst are redundant, but we can’t say the same for the pair area_se and texture_worst.",12259
+heart_correlation_heatmap.png,"Variables age and restecg are redundant, but we can’t say the same for the pair thalach and exang.",3486
+vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and MAJORKURTOSIS are redundant.,5979
+adult_correlation_heatmap.png,Variables hours-per-week and capital-gain are redundant.,1150
+adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair race and capital-loss.",1264
+diabetes_correlation_heatmap.png,"Variables Pregnancies and Insulin are redundant, but we can’t say the same for the pair Glucose and BloodPressure.",10829
+diabetes_correlation_heatmap.png,"Variables Insulin and Age are redundant, but we can’t say the same for the pair Pregnancies and BloodPressure.",10831
+diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair SkinThickness and BMI.",10898
+Wine_correlation_heatmap.png,Variables Malic acid and OD280-OD315 of diluted wines seem to be useful for classification tasks.,8727
+vehicle_correlation_heatmap.png,Variables target and LENGTHRECTANGULAR are redundant.,5856
+Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair Balance and EstimatedSalary.",2372
+Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Proanthocyanins and Hue.",9199
+vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and LENGTHRECTANGULAR are redundant.,5852
+heart_correlation_heatmap.png,Variables chol and trestbps seem to be useful for classification tasks.,3148
+heart_correlation_heatmap.png,"Variables cp and chol are redundant, but we can’t say the same for the pair age and restecg.",3460
+Wine_correlation_heatmap.png,Variables Total phenols and Proanthocyanins seem to be useful for classification tasks.,8697
+Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and EstimatedSalary are redundant, but we can’t say the same for the pair Geography and Gender.",2261
+Churn_Modelling_correlation_heatmap.png,Variables CreditScore and Balance are redundant.,2204
+vehicle_correlation_heatmap.png,"Variables MAJORVARIANCE and MAJORSKEWNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORVARIANCE.",6118
+heart_correlation_heatmap.png,Variables trestbps and thal are redundant.,3422
+vehicle_correlation_heatmap.png,Variables MAJORSKEWNESS and MAJORVARIANCE seem to be useful for classification tasks.,5280
+Wine_correlation_heatmap.png,"Variables Malic acid and Flavanoids are redundant, but we can’t say the same for the pair Color intensity and OD280-OD315 of diluted wines.",9243
+diabetes_correlation_heatmap.png,"Variables Pregnancies and SkinThickness are redundant, but we can’t say the same for the pair BMI and DiabetesPedigreeFunction.",10802
+vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MAX LENGTH ASPECT RATIO are redundant.,5773
+Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Color intensity are redundant.,8999
+vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORKURTOSIS are redundant, but we can’t say the same for the pair COMPACTNESS and PR AXIS ASPECT RATIO.",6247
+diabetes_correlation_heatmap.png,"Variables SkinThickness and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Insulin and BMI.",10871
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS are redundant, but we can’t say the same for the pair SCATTER RATIO and MAJORSKEWNESS.",6314
+vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and DISTANCE CIRCULARITY seem to be useful for classification tasks.,5140
+vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and MAJORSKEWNESS seem to be useful for classification tasks.,5332
+heart_correlation_heatmap.png,"Variables sex and restecg are redundant, but we can’t say the same for the pair cp and oldpeak.",3603
+Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and smoothness_se are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_se.",12239
+heart_correlation_heatmap.png,Variables thalach and ca are redundant.,3416
+heart_correlation_heatmap.png,"Variables oldpeak and thal are redundant, but we can’t say the same for the pair restecg and ca.",3579
+Wine_correlation_heatmap.png,"Variables Alcohol and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Flavanoids and Color intensity.",9040
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and SCATTER RATIO are redundant, but we can’t say the same for the pair PR AXISRECTANGULAR and LENGTHRECTANGULAR.",6586
+vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and MAJORVARIANCE seem to be useful for classification tasks.,5284
+Churn_Modelling_correlation_heatmap.png,Variables Tenure and Balance seem to be useful for classification tasks.,2097
+Wine_correlation_heatmap.png,"Variables Malic acid and Proanthocyanins are redundant, but we can’t say the same for the pair Ash and Total phenols.",9149
+Wine_correlation_heatmap.png,"Variables Proanthocyanins and Hue are redundant, but we can’t say the same for the pair Alcohol and Flavanoids.",9236
+adult_correlation_heatmap.png,Variables age and capital-loss are redundant.,1151
+vehicle_correlation_heatmap.png,"Variables GYRATIONRADIUS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair RADIUS RATIO and PR AXISRECTANGULAR.",6279
+Iris_correlation_heatmap.png,Variables SepalLengthCm and PetalWidthCm are redundant.,7911
+Churn_Modelling_correlation_heatmap.png,Variables Age and Balance are redundant.,2205
+adult_correlation_heatmap.png,"Variables gender and capital-gain are redundant, but we can’t say the same for the pair marital-status and capital-loss.",1470
+adult_correlation_heatmap.png,"Variables workclass and educational-num are redundant, but we can’t say the same for the pair relationship and gender.",1345
+vehicle_correlation_heatmap.png,Variables RADIUS RATIO and target are redundant.,6004
+vehicle_correlation_heatmap.png,"Variables COMPACTNESS and PR AXISRECTANGULAR are redundant, but we can’t say the same for the pair SCATTER RATIO and LENGTHRECTANGULAR.",6049
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and HOLLOWS RATIO are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORKURTOSIS.",6366
+vehicle_correlation_heatmap.png,Variables CIRCULARITY and SCATTER RATIO seem to be useful for classification tasks.,5197
+heart_correlation_heatmap.png,"Variables trestbps and fbs are redundant, but we can’t say the same for the pair cp and thal.",3523
+heart_correlation_heatmap.png,"Variables chol and restecg are redundant, but we can’t say the same for the pair cp and thal.",3608
+heart_correlation_heatmap.png,Variables chol and thalach are redundant.,3387
+adult_correlation_heatmap.png,Variables capital-gain and age seem to be useful for classification tasks.,1024
+Churn_Modelling_correlation_heatmap.png,"Variables Gender and Tenure are redundant, but we can’t say the same for the pair Balance and EstimatedSalary.",2240
+Wine_correlation_heatmap.png,Variables Color intensity and Class seem to be useful for classification tasks.,8612
+Wine_correlation_heatmap.png,"Variables Total phenols and Hue are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Color intensity.",9155
+vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and GYRATIONRADIUS seem to be useful for classification tasks.,5306
+adult_correlation_heatmap.png,"Variables workclass and marital-status are redundant, but we can’t say the same for the pair educational-num and capital-loss.",1406
+adult_correlation_heatmap.png,"Variables education and marital-status are redundant, but we can’t say the same for the pair fnlwgt and gender.",1277
+diabetes_correlation_heatmap.png,"Variables Glucose and Insulin are redundant, but we can’t say the same for the pair DiabetesPedigreeFunction and Age.",10901
+Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and area_se.",12250
+vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and target seem to be useful for classification tasks.,5414
+diabetes_correlation_heatmap.png,Variables SkinThickness and BloodPressure seem to be useful for classification tasks.,10592
+adult_correlation_heatmap.png,Variables fnlwgt and capital-loss seem to be useful for classification tasks.,1043
+vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORSKEWNESS.",6457
+vehicle_correlation_heatmap.png,Variables RADIUS RATIO and ELONGATEDNESS are redundant.,5806
+vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair CIRCULARITY and SCATTER RATIO.",6305
+vehicle_correlation_heatmap.png,Variables MINORKURTOSIS and PR AXISRECTANGULAR are redundant.,5835
+diabetes_correlation_heatmap.png,Variables DiabetesPedigreeFunction and Insulin seem to be useful for classification tasks.,10609
+Breast_Cancer_correlation_heatmap.png,Variables perimeter_worst and area_se are redundant.,12180
+adult_correlation_heatmap.png,"Variables age and gender are redundant, but we can’t say the same for the pair fnlwgt and education.",1357
+vehicle_correlation_heatmap.png,Variables RADIUS RATIO and HOLLOWS RATIO are redundant.,5986
+Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and radius_worst are redundant, but we can’t say the same for the pair perimeter_se and area_se.",12393
+adult_correlation_heatmap.png,"Variables education and gender are redundant, but we can’t say the same for the pair fnlwgt and hours-per-week.",1294
+vehicle_correlation_heatmap.png,"Variables COMPACTNESS and SCATTER RATIO are redundant, but we can’t say the same for the pair MINORVARIANCE and MINORSKEWNESS.",6330
+heart_correlation_heatmap.png,"Variables cp and slope are redundant, but we can’t say the same for the pair fbs and thalach.",3559
+vehicle_correlation_heatmap.png,"Variables LENGTHRECTANGULAR and MAJORKURTOSIS are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6046
+adult_correlation_heatmap.png,"Variables marital-status and gender are redundant, but we can’t say the same for the pair age and fnlwgt.",1275
+vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and SCATTER RATIO are redundant.,5794
+Breast_Cancer_correlation_heatmap.png,"Variables texture_worst and perimeter_worst are redundant, but we can’t say the same for the pair perimeter_mean and radius_worst.",12311
+diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Insulin and Age.",10820
+Wine_correlation_heatmap.png,Variables Color intensity and Flavanoids are redundant.,8967
+Breast_Cancer_correlation_heatmap.png,"Variables symmetry_se and texture_worst are redundant, but we can’t say the same for the pair area_se and perimeter_worst.",12392
+vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAX LENGTH ASPECT RATIO are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORKURTOSIS.",6082
+vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORKURTOSIS seem to be useful for classification tasks.,5365
+adult_correlation_heatmap.png,"Variables marital-status and capital-gain are redundant, but we can’t say the same for the pair race and hours-per-week.",1287
+vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and MINORVARIANCE seem to be useful for classification tasks.,5293
+heart_correlation_heatmap.png,"Variables sex and exang are redundant, but we can’t say the same for the pair fbs and thal.",3622
+vehicle_correlation_heatmap.png,"Variables MINORVARIANCE and MINORSKEWNESS are redundant, but we can’t say the same for the pair PR AXIS ASPECT RATIO and MAJORSKEWNESS.",6135
+heart_correlation_heatmap.png,"Variables chol and oldpeak are redundant, but we can’t say the same for the pair trestbps and thalach.",3664
+adult_correlation_heatmap.png,"Variables occupation and capital-gain are redundant, but we can’t say the same for the pair workclass and education.",1255
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and LENGTHRECTANGULAR.",6301
+Wine_correlation_heatmap.png,Variables Alcohol and Total phenols are redundant.,8949
+Wine_correlation_heatmap.png,Variables Proanthocyanins and Flavanoids seem to be useful for classification tasks.,8677
+Breast_Cancer_correlation_heatmap.png,"Variables texture_se and area_se are redundant, but we can’t say the same for the pair texture_mean and texture_worst.",12366
+vehicle_correlation_heatmap.png,Variables ELONGATEDNESS and RADIUS RATIO are redundant.,5737
+adult_correlation_heatmap.png,"Variables workclass and fnlwgt are redundant, but we can’t say the same for the pair educational-num and occupation.",1340
+adult_correlation_heatmap.png,"Variables relationship and capital-loss are redundant, but we can’t say the same for the pair fnlwgt and race.",1206
+Wine_correlation_heatmap.png,"Variables Total phenols and Color intensity are redundant, but we can’t say the same for the pair Flavanoids and Proanthocyanins.",9028
+Breast_Cancer_correlation_heatmap.png,"Variables area_se and perimeter_worst are redundant, but we can’t say the same for the pair smoothness_se and texture_worst.",12307
+heart_correlation_heatmap.png,Variables ca and oldpeak are redundant.,3400
+heart_correlation_heatmap.png,"Variables thalach and exang are redundant, but we can’t say the same for the pair chol and thal.",3672
+Churn_Modelling_correlation_heatmap.png,"Variables Age and Tenure are redundant, but we can’t say the same for the pair Balance and HasCrCard.",2288
+adult_correlation_heatmap.png,"Variables age and hours-per-week are redundant, but we can’t say the same for the pair capital-gain and capital-loss.",1184
+Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and OD280-OD315 of diluted wines are redundant.,9021
+vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and MAJORKURTOSIS are redundant, but we can’t say the same for the pair DISTANCE CIRCULARITY and MINORVARIANCE.",6382
+Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair Gender and Age.",2398
+Breast_Cancer_correlation_heatmap.png,"Variables smoothness_se and perimeter_worst are redundant, but we can’t say the same for the pair area_se and symmetry_se.",12261
+adult_correlation_heatmap.png,Variables fnlwgt and capital-gain are redundant.,1147
+Wine_correlation_heatmap.png,"Variables Malic acid and Total phenols are redundant, but we can’t say the same for the pair Nonflavanoid phenols and Hue.",9223
+heart_correlation_heatmap.png,Variables age and restecg are redundant.,3375
+diabetes_correlation_heatmap.png,Variables Glucose and Age are redundant.,10787
+adult_correlation_heatmap.png,"Variables educational-num and occupation are redundant, but we can’t say the same for the pair fnlwgt and marital-status.",1307
+vehicle_correlation_heatmap.png,"Variables CIRCULARITY and DISTANCE CIRCULARITY are redundant, but we can’t say the same for the pair ELONGATEDNESS and MAJORSKEWNESS.",6144
+vehicle_correlation_heatmap.png,"Variables MINORKURTOSIS and HOLLOWS RATIO are redundant, but we can’t say the same for the pair MAJORVARIANCE and MAJORKURTOSIS.",6460
+vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORKURTOSIS seem to be useful for classification tasks.,5384
+vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MINORSKEWNESS and MINORKURTOSIS.",6317
+diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and BMI.",10857
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair SCATTER RATIO and GYRATIONRADIUS.",6112
+Wine_correlation_heatmap.png,Variables Class and Color intensity are redundant.,8992
+heart_correlation_heatmap.png,"Variables age and cp are redundant, but we can’t say the same for the pair thalach and ca.",3546
+adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair education and marital-status.",1382
+Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and perimeter_se seem to be useful for classification tasks.,11943
+adult_correlation_heatmap.png,"Variables education and relationship are redundant, but we can’t say the same for the pair marital-status and occupation.",1428
+vehicle_correlation_heatmap.png,Variables PR AXISRECTANGULAR and MAJORVARIANCE are redundant.,5865
+heart_correlation_heatmap.png,Variables thal and oldpeak are redundant.,3401
+adult_correlation_heatmap.png,Variables educational-num and hours-per-week seem to be useful for classification tasks.,1049
+Wine_correlation_heatmap.png,Variables Alcalinity of ash and Color intensity seem to be useful for classification tasks.,8707
+diabetes_correlation_heatmap.png,Variables Glucose and Age seem to be useful for classification tasks.,10626
+Breast_Cancer_correlation_heatmap.png,Variables smoothness_se and texture_worst seem to be useful for classification tasks.,11992
+adult_correlation_heatmap.png,"Variables marital-status and capital-loss are redundant, but we can’t say the same for the pair age and occupation.",1424
+vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and SCATTER RATIO seem to be useful for classification tasks.,5205
+Churn_Modelling_correlation_heatmap.png,"Variables Geography and HasCrCard are redundant, but we can’t say the same for the pair Gender and Tenure.",2227
+Churn_Modelling_correlation_heatmap.png,"Variables CreditScore and Tenure are redundant, but we can’t say the same for the pair Gender and NumOfProducts.",2323
+Breast_Cancer_correlation_heatmap.png,"Variables texture_mean and area_se are redundant, but we can’t say the same for the pair radius_worst and texture_worst.",12325
+Wine_correlation_heatmap.png,"Variables Hue and OD280-OD315 of diluted wines are redundant, but we can’t say the same for the pair Malic acid and Nonflavanoid phenols.",9065
+Churn_Modelling_correlation_heatmap.png,Variables EstimatedSalary and Tenure seem to be useful for classification tasks.,2094
+diabetes_correlation_heatmap.png,"Variables Pregnancies and SkinThickness are redundant, but we can’t say the same for the pair Insulin and BMI.",10868
+Churn_Modelling_correlation_heatmap.png,Variables NumOfProducts and Age seem to be useful for classification tasks.,2088
+adult_correlation_heatmap.png,"Variables relationship and race are redundant, but we can’t say the same for the pair age and capital-gain.",1220
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair GYRATIONRADIUS and HOLLOWS RATIO.",6344
+vehicle_correlation_heatmap.png,Variables MAX LENGTH ASPECT RATIO and MINORVARIANCE seem to be useful for classification tasks.,5291
+Wine_correlation_heatmap.png,"Variables Flavanoids and Color intensity are redundant, but we can’t say the same for the pair Alcalinity of ash and Proanthocyanins.",9142
+Breast_Cancer_correlation_heatmap.png,Variables texture_se and area_se are redundant.,12174
+adult_correlation_heatmap.png,Variables educational-num and capital-loss seem to be useful for classification tasks.,1044
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and RADIUS RATIO are redundant, but we can’t say the same for the pair MAX LENGTH ASPECT RATIO and PR AXISRECTANGULAR.",6380
+Breast_Cancer_correlation_heatmap.png,"Variables perimeter_se and symmetry_se are redundant, but we can’t say the same for the pair perimeter_mean and perimeter_worst.",12341
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORSKEWNESS are redundant, but we can’t say the same for the pair MAJORVARIANCE and MINORSKEWNESS.",6253
+vehicle_correlation_heatmap.png,Variables GYRATIONRADIUS and PR AXIS ASPECT RATIO are redundant.,5760
+heart_correlation_heatmap.png,Variables cp and thalach are redundant.,3385
+vehicle_correlation_heatmap.png,Variables HOLLOWS RATIO and LENGTHRECTANGULAR seem to be useful for classification tasks.,5266
+heart_correlation_heatmap.png,Variables slope and thalach seem to be useful for classification tasks.,3179
+Wine_correlation_heatmap.png,"Variables Flavanoids and Color intensity are redundant, but we can’t say the same for the pair Malic acid and OD280-OD315 of diluted wines.",9086
+diabetes_correlation_heatmap.png,"Variables Glucose and Age are redundant, but we can’t say the same for the pair BloodPressure and DiabetesPedigreeFunction.",10846
+vehicle_correlation_heatmap.png,Variables SCATTER RATIO and HOLLOWS RATIO seem to be useful for classification tasks.,5400
+Churn_Modelling_correlation_heatmap.png,"Variables Age and Balance are redundant, but we can’t say the same for the pair Geography and EstimatedSalary.",2242
+adult_correlation_heatmap.png,"Variables capital-gain and capital-loss are redundant, but we can’t say the same for the pair race and gender.",1203
+vehicle_correlation_heatmap.png,"Variables ELONGATEDNESS and MAJORSKEWNESS are redundant, but we can’t say the same for the pair SCATTER RATIO and HOLLOWS RATIO.",6355
+diabetes_correlation_heatmap.png,"Variables SkinThickness and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair BloodPressure and BMI.",10848
+heart_correlation_heatmap.png,Variables cp and slope are redundant.,3403
+diabetes_correlation_heatmap.png,"Variables Pregnancies and DiabetesPedigreeFunction are redundant, but we can’t say the same for the pair Glucose and BloodPressure.",10838
+vehicle_correlation_heatmap.png,Variables MAJORVARIANCE and GYRATIONRADIUS are redundant.,5903
+heart_correlation_heatmap.png,Variables thalach and oldpeak seem to be useful for classification tasks.,3187
+adult_correlation_heatmap.png,"Variables age and hours-per-week are redundant, but we can’t say the same for the pair workclass and gender.",1360
+adult_correlation_heatmap.png,Variables capital-loss and age are redundant.,1134
+Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Class seem to be useful for classification tasks.,8610
+Breast_Cancer_correlation_heatmap.png,"Variables texture_se and perimeter_se are redundant, but we can’t say the same for the pair perimeter_mean and texture_worst.",12242
+vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and GYRATIONRADIUS seem to be useful for classification tasks.,5317
+vehicle_correlation_heatmap.png,"Variables DISTANCE CIRCULARITY and GYRATIONRADIUS are redundant, but we can’t say the same for the pair MINORSKEWNESS and HOLLOWS RATIO.",6296
+vehicle_correlation_heatmap.png,"Variables MAX LENGTH ASPECT RATIO and MINORKURTOSIS are redundant, but we can’t say the same for the pair SCATTER RATIO and MINORSKEWNESS.",6203
+Breast_Cancer_correlation_heatmap.png,Variables symmetry_se and perimeter_mean seem to be useful for classification tasks.,11929
+Wine_correlation_heatmap.png,Variables Nonflavanoid phenols and Color intensity seem to be useful for classification tasks.,8710
+adult_correlation_heatmap.png,"Variables fnlwgt and educational-num are redundant, but we can’t say the same for the pair relationship and race.",1457
+heart_correlation_heatmap.png,"Variables trestbps and slope are redundant, but we can’t say the same for the pair restecg and exang.",3588
+vehicle_correlation_heatmap.png,Variables COMPACTNESS and PR AXIS ASPECT RATIO seem to be useful for classification tasks.,5160
+vehicle_correlation_heatmap.png,Variables RADIUS RATIO and DISTANCE CIRCULARITY are redundant.,5715
+vehicle_correlation_heatmap.png,Variables DISTANCE CIRCULARITY and MAJORVARIANCE seem to be useful for classification tasks.,5270
+Wine_correlation_heatmap.png,"Variables Alcohol and Alcalinity of ash are redundant, but we can’t say the same for the pair Proanthocyanins and OD280-OD315 of diluted wines.",9216
+vehicle_correlation_heatmap.png,"Variables PR AXIS ASPECT RATIO and MAJORKURTOSIS are redundant, but we can’t say the same for the pair RADIUS RATIO and SCATTER RATIO.",6493
+adult_correlation_heatmap.png,"Variables race and capital-loss are redundant, but we can’t say the same for the pair workclass and educational-num.",1209
+vehicle_correlation_heatmap.png,Variables MINORSKEWNESS and RADIUS RATIO seem to be useful for classification tasks.,5155
+heart_correlation_heatmap.png,"Variables trestbps and ca are redundant, but we can’t say the same for the pair restecg and exang.",3596
+Wine_correlation_heatmap.png,"Variables Alcalinity of ash and Color intensity are redundant, but we can’t say the same for the pair Alcohol and Proanthocyanins.",9202
+Breast_Cancer_correlation_heatmap.png,Variables perimeter_mean and perimeter_worst are redundant.,12218
+vehicle_correlation_heatmap.png,Variables LENGTHRECTANGULAR and GYRATIONRADIUS are redundant.,5902
+heart_correlation_heatmap.png,"Variables oldpeak and thal are redundant, but we can’t say the same for the pair cp and fbs.",3513
+Wine_correlation_heatmap.png,"Variables Ash and Nonflavanoid phenols are redundant, but we can’t say the same for the pair Alcalinity of ash and Total phenols.",9126
+Wine_correlation_heatmap.png,Variables Proanthocyanins and Hue seem to be useful for classification tasks.,8722
+Wine_correlation_heatmap.png,"Variables Total phenols and Flavanoids are redundant, but we can’t say the same for the pair Alcohol and Malic acid.",9100
+heart_correlation_heatmap.png,Variables trestbps and oldpeak seem to be useful for classification tasks.,3184
+Wine_correlation_heatmap.png,Variables OD280-OD315 of diluted wines and Nonflavanoid phenols seem to be useful for classification tasks.,8691
+vehicle_correlation_heatmap.png,"Variables MAJORSKEWNESS and MINORSKEWNESS are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MAJORKURTOSIS.",6198
+vehicle_correlation_heatmap.png,"Variables SCATTER RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair ELONGATEDNESS and LENGTHRECTANGULAR.",6511
+Iris_correlation_heatmap.png,Variables SepalLengthCm and SepalWidthCm seem to be useful for classification tasks.,7840
+Churn_Modelling_correlation_heatmap.png,"Variables Gender and IsActiveMember are redundant, but we can’t say the same for the pair Geography and EstimatedSalary.",2351
+vehicle_correlation_heatmap.png,"Variables RADIUS RATIO and MAJORVARIANCE are redundant, but we can’t say the same for the pair LENGTHRECTANGULAR and MINORSKEWNESS.",6195
+Wine_correlation_heatmap.png,Variables Alcohol and Hue are redundant.,9004
+adult_correlation_heatmap.png,"Variables educational-num and hours-per-week are redundant, but we can’t say the same for the pair education and race.",1191
+vehicle_correlation_heatmap.png,Variables PR AXIS ASPECT RATIO and GYRATIONRADIUS seem to be useful for classification tasks.,5308
+heart_correlation_heatmap.png,Variables trestbps and chol are redundant.,3368
+vehicle_correlation_heatmap.png,"Variables CIRCULARITY and MINORVARIANCE are redundant, but we can’t say the same for the pair MAJORSKEWNESS and HOLLOWS RATIO.",6045
+heart_correlation_heatmap.png,"Variables cp and exang are redundant, but we can’t say the same for the pair chol and fbs.",3696
+diabetes_decision_tree.png,The variable DiabetesPedigreeFunction seems to be one of the four most relevant features.,10694
+heart_decision_tree.png,The variable slope seems to be one of the two most relevant features.,3265
+BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the five most relevant features.,10010
+vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the three most relevant features.,5559
+vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the two most relevant features.,5531
+adult_decision_tree.png,The variable fnlwgt seems to be one of the four most relevant features.,1095
+Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the four most relevant features.,8828
+vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the four most relevant features.,5571
+adult_decision_tree.png,The variable capital-loss seems to be one of the four most relevant features.,1098
+Wine_decision_tree.png,The variable Total phenols seems to be one of the two most relevant features.,8801
+Iris_decision_tree.png,The variable SepalWidthCm seems to be one of the three most relevant features.,7874
+vehicle_decision_tree.png,The variable MAJORKURTOSIS seems to be one of the two most relevant features.,5541
+vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the three most relevant features.,5548
+Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the five most relevant features.,12085
+vehicle_decision_tree.png,The variable COMPACTNESS seems to be one of the four most relevant features.,5563
+vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the three most relevant features.,5554
+BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the four most relevant features.,10009
+Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the four most relevant features.,8827
+heart_decision_tree.png,The variable chol seems to be one of the five most relevant features.,3291
+vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the two most relevant features.,5540
+Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the five most relevant features.,12089
+Churn_Modelling_decision_tree.png,The variable Tenure seems to be one of the five most relevant features.,2160
+Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the three most relevant features.,12072
+Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the four most relevant features.,12079
+Wine_decision_tree.png,The variable Hue seems to be one of the three most relevant features.,8818
+vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the three most relevant features.,5558
+adult_decision_tree.png,The variable capital-gain seems to be one of the two most relevant features.,1085
+vehicle_decision_tree.png,The variable COMPACTNESS seems to be one of the five most relevant features.,5582
+Churn_Modelling_decision_tree.png,The variable NumOfProducts seems to be one of the five most relevant features.,2162
+Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the four most relevant features.,7880
+diabetes_decision_tree.png,The variable Age seems to be one of the five most relevant features.,10703
+vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the four most relevant features.,5577
+diabetes_decision_tree.png,The variable BMI seems to be one of the five most relevant features.,10701
+heart_decision_tree.png,The variable age seems to be one of the two most relevant features.,3258
+Wine_decision_tree.png,The variable Malic acid seems to be one of the five most relevant features.,8834
+Wine_decision_tree.png,The variable Color intensity seems to be one of the five most relevant features.,8841
+heart_decision_tree.png,The variable thalach seems to be one of the four most relevant features.,3283
+Iris_decision_tree.png,The variable SepalWidthCm seems to be one of the five most relevant features.,7882
+vehicle_decision_tree.png,The variable target seems to be one of the five most relevant features.,5600
+Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the four most relevant features.,2152
+vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the four most relevant features.,5566
+diabetes_decision_tree.png,The variable BloodPressure seems to be one of the five most relevant features.,10698
+heart_decision_tree.png,The variable thal seems to be one of the three most relevant features.,3277
+diabetes_decision_tree.png,The variable Glucose seems to be one of the three most relevant features.,10681
+adult_decision_tree.png,The variable capital-gain seems to be one of the five most relevant features.,1103
+adult_decision_tree.png,The variable hours-per-week seems to be one of the two most relevant features.,1087
+heart_decision_tree.png,The variable oldpeak seems to be one of the two most relevant features.,3264
+Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the two most relevant features.,8803
+Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the two most relevant features.,12062
+Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the two most relevant features.,12061
+vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the two most relevant features.,5536
+vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the two most relevant features.,5534
+Wine_decision_tree.png,The variable OD280-OD315 of diluted wines seems to be one of the three most relevant features.,8819
+heart_decision_tree.png,The variable oldpeak seems to be one of the five most relevant features.,3294
+heart_decision_tree.png,The variable chol seems to be one of the three most relevant features.,3271
+vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the five most relevant features.,5585
+adult_decision_tree.png,The variable capital-loss seems to be one of the five most relevant features.,1104
+adult_decision_tree.png,The variable fnlwgt seems to be one of the two most relevant features.,1083
+Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the two most relevant features.,12064
+vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the four most relevant features.,5565
+diabetes_decision_tree.png,The variable DiabetesPedigreeFunction seems to be one of the five most relevant features.,10702
+vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the two most relevant features.,5526
+Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the three most relevant features.,12071
+vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the four most relevant features.,5564
+Wine_decision_tree.png,The variable Flavanoids seems to be one of the four most relevant features.,8826
+heart_decision_tree.png,The variable age seems to be one of the four most relevant features.,3278
+vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the two most relevant features.,5535
+vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the four most relevant features.,5576
+heart_decision_tree.png,The variable thal seems to be one of the four most relevant features.,3287
+Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the four most relevant features.,12080
+Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the three most relevant features.,7875
+BankNoteAuthentication_decision_tree.png,The variable skewness seems to be one of the two most relevant features.,9999
+Churn_Modelling_decision_tree.png,The variable Age seems to be one of the three most relevant features.,2147
+heart_decision_tree.png,The variable trestbps seems to be one of the five most relevant features.,3290
+BankNoteAuthentication_decision_tree.png,The variable skewness seems to be one of the five most relevant features.,10011
+Wine_decision_tree.png,The variable Ash seems to be one of the four most relevant features.,8823
+Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the three most relevant features.,2151
+Wine_decision_tree.png,The variable Alcalinity of ash seems to be one of the four most relevant features.,8824
+vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the five most relevant features.,5590
+vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the five most relevant features.,5586
+diabetes_decision_tree.png,The variable Insulin seems to be one of the five most relevant features.,10700
+Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the four most relevant features.,12077
+BankNoteAuthentication_decision_tree.png,The variable skewness seems to be one of the four most relevant features.,10007
+Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the five most relevant features.,7883
+Churn_Modelling_decision_tree.png,The variable Age seems to be one of the four most relevant features.,2153
+vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the five most relevant features.,5584
+diabetes_decision_tree.png,The variable Age seems to be one of the four most relevant features.,10695
+BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the two most relevant features.,9998
+adult_decision_tree.png,The variable capital-gain seems to be one of the three most relevant features.,1091
+heart_decision_tree.png,The variable slope seems to be one of the four most relevant features.,3285
+Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the five most relevant features.,2158
+vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the four most relevant features.,5569
+adult_decision_tree.png,The variable capital-loss seems to be one of the three most relevant features.,1092
+vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the three most relevant features.,5546
+vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the five most relevant features.,5599
+Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the three most relevant features.,12067
+adult_decision_tree.png,The variable fnlwgt seems to be one of the five most relevant features.,1101
+Wine_decision_tree.png,The variable Alcohol seems to be one of the four most relevant features.,8821
+adult_decision_tree.png,The variable age seems to be one of the five most relevant features.,1100
+diabetes_decision_tree.png,The variable Pregnancies seems to be one of the two most relevant features.,10672
+vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the two most relevant features.,5538
+Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the five most relevant features.,12087
+Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the five most relevant features.,2163
+Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the three most relevant features.,2146
+Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the five most relevant features.,8840
+vehicle_decision_tree.png,The variable DISTANCE CIRCULARITY seems to be one of the two most relevant features.,5527
+Wine_decision_tree.png,The variable Flavanoids seems to be one of the five most relevant features.,8838
+Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the two most relevant features.,12063
+heart_decision_tree.png,The variable age seems to be one of the five most relevant features.,3288
+diabetes_decision_tree.png,The variable Glucose seems to be one of the two most relevant features.,10673
+diabetes_decision_tree.png,The variable BloodPressure seems to be one of the four most relevant features.,10690
+Wine_decision_tree.png,The variable Class seems to be one of the five most relevant features.,8832
+Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the five most relevant features.,12093
+vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the four most relevant features.,5580
+Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the three most relevant features.,12065
+Wine_decision_tree.png,The variable OD280-OD315 of diluted wines seems to be one of the five most relevant features.,8843
+Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the four most relevant features.,12083
+diabetes_decision_tree.png,The variable Glucose seems to be one of the four most relevant features.,10689
+vehicle_decision_tree.png,The variable GYRATIONRADIUS seems to be one of the four most relevant features.,5575
+Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the three most relevant features.,7876
+heart_decision_tree.png,The variable thalach seems to be one of the two most relevant features.,3263
+vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the three most relevant features.,5551
+Iris_decision_tree.png,The variable SepalWidthCm seems to be one of the two most relevant features.,7870
+Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the five most relevant features.,12092
+Breast_Cancer_decision_tree.png,The variable texture_worst seems to be one of the three most relevant features.,12073
+Wine_decision_tree.png,The variable Alcohol seems to be one of the three most relevant features.,8809
+heart_decision_tree.png,The variable thal seems to be one of the two most relevant features.,3267
+heart_decision_tree.png,The variable thalach seems to be one of the three most relevant features.,3273
+Wine_decision_tree.png,The variable Color intensity seems to be one of the three most relevant features.,8817
+diabetes_decision_tree.png,The variable BMI seems to be one of the two most relevant features.,10677
+adult_decision_tree.png,The variable hours-per-week seems to be one of the three most relevant features.,1093
+Wine_decision_tree.png,The variable OD280-OD315 of diluted wines seems to be one of the two most relevant features.,8807
+Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the four most relevant features.,12084
+BankNoteAuthentication_decision_tree.png,The variable curtosis seems to be one of the five most relevant features.,10012
+Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the three most relevant features.,12068
+Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the four most relevant features.,7879
+Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the five most relevant features.,8839
+diabetes_decision_tree.png,The variable SkinThickness seems to be one of the three most relevant features.,10683
+Wine_decision_tree.png,The variable Ash seems to be one of the five most relevant features.,8835
+Wine_decision_tree.png,The variable Flavanoids seems to be one of the two most relevant features.,8802
+Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the four most relevant features.,12081
+diabetes_decision_tree.png,The variable BloodPressure seems to be one of the three most relevant features.,10682
+adult_decision_tree.png,The variable fnlwgt seems to be one of the three most relevant features.,1089
+Breast_Cancer_decision_tree.png,The variable radius_worst seems to be one of the four most relevant features.,12082
+Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the five most relevant features.,12088
+diabetes_decision_tree.png,The variable Insulin seems to be one of the three most relevant features.,10684
+Breast_Cancer_decision_tree.png,The variable symmetry_se seems to be one of the five most relevant features.,12091
+vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the five most relevant features.,5592
+adult_decision_tree.png,The variable hours-per-week seems to be one of the four most relevant features.,1099
+BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the four most relevant features.,10006
+BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the three most relevant features.,10005
+vehicle_decision_tree.png,The variable MAJORKURTOSIS seems to be one of the three most relevant features.,5560
+vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the three most relevant features.,5555
+vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the four most relevant features.,5567
+adult_decision_tree.png,The variable educational-num seems to be one of the three most relevant features.,1090
+Churn_Modelling_decision_tree.png,The variable CreditScore seems to be one of the two most relevant features.,2140
+vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the five most relevant features.,5595
+Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the five most relevant features.,12090
+Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the two most relevant features.,12060
+heart_decision_tree.png,The variable restecg seems to be one of the three most relevant features.,3272
+vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the two most relevant features.,5539
+heart_decision_tree.png,The variable cp seems to be one of the four most relevant features.,3279
+vehicle_decision_tree.png,The variable MAX LENGTH ASPECT RATIO seems to be one of the two most relevant features.,5530
+diabetes_decision_tree.png,The variable Insulin seems to be one of the four most relevant features.,10692
+diabetes_decision_tree.png,The variable Pregnancies seems to be one of the five most relevant features.,10696
+vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the three most relevant features.,5552
+Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the two most relevant features.,8804
+Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the five most relevant features.,12094
+Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the two most relevant features.,12058
+vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the five most relevant features.,5593
+vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the four most relevant features.,5578
+Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the four most relevant features.,12075
+diabetes_decision_tree.png,The variable Age seems to be one of the two most relevant features.,10679
+vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the four most relevant features.,5570
+Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the three most relevant features.,2149
+Churn_Modelling_decision_tree.png,The variable NumOfProducts seems to be one of the four most relevant features.,2156
+diabetes_decision_tree.png,The variable BMI seems to be one of the four most relevant features.,10693
+vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the two most relevant features.,5542
+Churn_Modelling_decision_tree.png,The variable Tenure seems to be one of the three most relevant features.,2148
+heart_decision_tree.png,The variable slope seems to be one of the three most relevant features.,3275
+Wine_decision_tree.png,The variable Malic acid seems to be one of the three most relevant features.,8810
+heart_decision_tree.png,The variable restecg seems to be one of the five most relevant features.,3292
+Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the five most relevant features.,2161
+vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the three most relevant features.,5553
+Wine_decision_tree.png,The variable Class seems to be one of the three most relevant features.,8808
+Wine_decision_tree.png,The variable Malic acid seems to be one of the two most relevant features.,8798
+Wine_decision_tree.png,The variable Nonflavanoid phenols seems to be one of the three most relevant features.,8815
+BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the two most relevant features.,10001
+adult_decision_tree.png,The variable age seems to be one of the three most relevant features.,1088
+adult_decision_tree.png,The variable age seems to be one of the two most relevant features.,1082
+diabetes_decision_tree.png,The variable Pregnancies seems to be one of the three most relevant features.,10680
+Wine_decision_tree.png,The variable Total phenols seems to be one of the five most relevant features.,8837
+Churn_Modelling_decision_tree.png,The variable Tenure seems to be one of the four most relevant features.,2154
+Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the three most relevant features.,12069
+heart_decision_tree.png,The variable trestbps seems to be one of the three most relevant features.,3270
+adult_decision_tree.png,The variable capital-gain seems to be one of the four most relevant features.,1097
+vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the five most relevant features.,5583
+diabetes_decision_tree.png,The variable BloodPressure seems to be one of the two most relevant features.,10674
+Wine_decision_tree.png,The variable Class seems to be one of the two most relevant features.,8796
+vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the two most relevant features.,5528
+heart_decision_tree.png,The variable oldpeak seems to be one of the four most relevant features.,3284
+Breast_Cancer_decision_tree.png,The variable texture_se seems to be one of the two most relevant features.,12057
+vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the two most relevant features.,5532
+BankNoteAuthentication_decision_tree.png,The variable variance seems to be one of the three most relevant features.,10002
+heart_decision_tree.png,The variable ca seems to be one of the three most relevant features.,3276
+Wine_decision_tree.png,The variable Malic acid seems to be one of the four most relevant features.,8822
+diabetes_decision_tree.png,The variable Insulin seems to be one of the two most relevant features.,10676
+BankNoteAuthentication_decision_tree.png,The variable curtosis seems to be one of the three most relevant features.,10004
+Breast_Cancer_decision_tree.png,The variable perimeter_worst seems to be one of the three most relevant features.,12074
+diabetes_decision_tree.png,The variable BMI seems to be one of the three most relevant features.,10685
+heart_decision_tree.png,The variable ca seems to be one of the five most relevant features.,3296
+vehicle_decision_tree.png,The variable PR AXIS ASPECT RATIO seems to be one of the two most relevant features.,5529
+vehicle_decision_tree.png,The variable MAJORKURTOSIS seems to be one of the four most relevant features.,5579
+Wine_decision_tree.png,The variable Alcalinity of ash seems to be one of the three most relevant features.,8812
+Churn_Modelling_decision_tree.png,The variable NumOfProducts seems to be one of the three most relevant features.,2150
+Breast_Cancer_decision_tree.png,The variable perimeter_se seems to be one of the four most relevant features.,12078
+Breast_Cancer_decision_tree.png,The variable smoothness_se seems to be one of the three most relevant features.,12070
+adult_decision_tree.png,The variable educational-num seems to be one of the five most relevant features.,1102
+vehicle_decision_tree.png,The variable RADIUS RATIO seems to be one of the three most relevant features.,5547
+Wine_decision_tree.png,The variable Hue seems to be one of the four most relevant features.,8830
+Wine_decision_tree.png,The variable Hue seems to be one of the two most relevant features.,8806
+vehicle_decision_tree.png,The variable ELONGATEDNESS seems to be one of the five most relevant features.,5589
+diabetes_decision_tree.png,The variable Age seems to be one of the three most relevant features.,10687
+vehicle_decision_tree.png,The variable MAJORSKEWNESS seems to be one of the three most relevant features.,5557
+vehicle_decision_tree.png,The variable MINORKURTOSIS seems to be one of the five most relevant features.,5597
+vehicle_decision_tree.png,The variable CIRCULARITY seems to be one of the three most relevant features.,5545
+Breast_Cancer_decision_tree.png,The variable texture_mean seems to be one of the two most relevant features.,12055
+Wine_decision_tree.png,The variable Total phenols seems to be one of the four most relevant features.,8825
+diabetes_decision_tree.png,The variable Glucose seems to be one of the five most relevant features.,10697
+Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the two most relevant features.,7869
+vehicle_decision_tree.png,The variable GYRATIONRADIUS seems to be one of the five most relevant features.,5594
+diabetes_decision_tree.png,The variable DiabetesPedigreeFunction seems to be one of the two most relevant features.,10678
+Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the two most relevant features.,7872
+vehicle_decision_tree.png,The variable MAX LENGTH ASPECT RATIO seems to be one of the four most relevant features.,5568
+Churn_Modelling_decision_tree.png,The variable Age seems to be one of the five most relevant features.,2159
+vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the five most relevant features.,5591
+Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the five most relevant features.,7881
+Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the four most relevant features.,2157
+heart_decision_tree.png,The variable restecg seems to be one of the two most relevant features.,3262
+Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the two most relevant features.,2143
+Breast_Cancer_decision_tree.png,The variable perimeter_mean seems to be one of the two most relevant features.,12056
+Wine_decision_tree.png,The variable Alcohol seems to be one of the five most relevant features.,8833
+vehicle_decision_tree.png,The variable COMPACTNESS seems to be one of the three most relevant features.,5544
+heart_decision_tree.png,The variable chol seems to be one of the four most relevant features.,3281
+Churn_Modelling_decision_tree.png,The variable Age seems to be one of the two most relevant features.,2141
+Churn_Modelling_decision_tree.png,The variable EstimatedSalary seems to be one of the two most relevant features.,2145
+heart_decision_tree.png,The variable slope seems to be one of the five most relevant features.,3295
+vehicle_decision_tree.png,The variable MAX LENGTH ASPECT RATIO seems to be one of the three most relevant features.,5549
+Breast_Cancer_decision_tree.png,The variable area_se seems to be one of the two most relevant features.,12059
+heart_decision_tree.png,The variable oldpeak seems to be one of the three most relevant features.,3274
+heart_decision_tree.png,The variable trestbps seems to be one of the two most relevant features.,3260
+heart_decision_tree.png,The variable age seems to be one of the three most relevant features.,3268
+heart_decision_tree.png,The variable ca seems to be one of the four most relevant features.,3286
+heart_decision_tree.png,The variable restecg seems to be one of the four most relevant features.,3282
+Wine_decision_tree.png,The variable Ash seems to be one of the two most relevant features.,8799
+vehicle_decision_tree.png,The variable MINORSKEWNESS seems to be one of the five most relevant features.,5596
+diabetes_decision_tree.png,The variable SkinThickness seems to be one of the two most relevant features.,10675
+Iris_decision_tree.png,The variable PetalLengthCm seems to be one of the two most relevant features.,7871
+vehicle_decision_tree.png,The variable MAJORVARIANCE seems to be one of the four most relevant features.,5573
+heart_decision_tree.png,The variable cp seems to be one of the three most relevant features.,3269
+adult_decision_tree.png,The variable educational-num seems to be one of the two most relevant features.,1084
+Wine_decision_tree.png,The variable Flavanoids seems to be one of the three most relevant features.,8814
+Breast_Cancer_decision_tree.png,The variable perimeter_mean seems to be one of the three most relevant features.,12066
+vehicle_decision_tree.png,The variable target seems to be one of the three most relevant features.,5562
+Wine_decision_tree.png,The variable Proanthocyanins seems to be one of the three most relevant features.,8816
+heart_decision_tree.png,The variable thal seems to be one of the five most relevant features.,3297
+adult_decision_tree.png,The variable hours-per-week seems to be one of the five most relevant features.,1105
+vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the three most relevant features.,5550
+vehicle_decision_tree.png,The variable target seems to be one of the four most relevant features.,5581
+adult_decision_tree.png,The variable capital-loss seems to be one of the two most relevant features.,1086
+Wine_decision_tree.png,The variable Alcalinity of ash seems to be one of the five most relevant features.,8836
+vehicle_decision_tree.png,The variable PR AXISRECTANGULAR seems to be one of the two most relevant features.,5533
+Breast_Cancer_decision_tree.png,The variable perimeter_mean seems to be one of the four most relevant features.,12076
+heart_decision_tree.png,The variable thalach seems to be one of the five most relevant features.,3293
+Wine_decision_tree.png,The variable Total phenols seems to be one of the three most relevant features.,8813
+BankNoteAuthentication_decision_tree.png,The variable curtosis seems to be one of the four most relevant features.,10008
+BankNoteAuthentication_decision_tree.png,The variable entropy seems to be one of the five most relevant features.,10013
+vehicle_decision_tree.png,The variable SCATTER RATIO seems to be one of the five most relevant features.,5588
+heart_decision_tree.png,The variable ca seems to be one of the two most relevant features.,3266
+vehicle_decision_tree.png,The variable MINORVARIANCE seems to be one of the four most relevant features.,5574
+diabetes_decision_tree.png,The variable SkinThickness seems to be one of the four most relevant features.,10691
+heart_decision_tree.png,The variable trestbps seems to be one of the four most relevant features.,3280
+vehicle_decision_tree.png,The variable LENGTHRECTANGULAR seems to be one of the four most relevant features.,5572
+Wine_decision_tree.png,The variable Ash seems to be one of the three most relevant features.,8811
+Churn_Modelling_decision_tree.png,The variable Balance seems to be one of the four most relevant features.,2155
+Iris_decision_tree.png,The variable PetalWidthCm seems to be one of the five most relevant features.,7884
+Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the four most relevant features.,7877
+Iris_decision_tree.png,The variable SepalLengthCm seems to be one of the three most relevant features.,7873
+vehicle_decision_tree.png,The variable GYRATIONRADIUS seems to be one of the three most relevant features.,5556
+vehicle_decision_tree.png,The variable HOLLOWS RATIO seems to be one of the three most relevant features.,5561
+Breast_Cancer_decision_tree.png,"The variable texture_se discriminates between the target values, as shown in the decision tree.",12047
+Wine_decision_tree.png,"The variable OD280-OD315 of diluted wines discriminates between the target values, as shown in the decision tree.",8795
+heart_decision_tree.png,"The variable chol discriminates between the target values, as shown in the decision tree.",3251
+Churn_Modelling_decision_tree.png,"The variable Age discriminates between the target values, as shown in the decision tree.",2135
+Iris_decision_tree.png,"The variable PetalWidthCm discriminates between the target values, as shown in the decision tree.",7868
+vehicle_decision_tree.png,"The variable COMPACTNESS discriminates between the target values, as shown in the decision tree.",5506
+Breast_Cancer_decision_tree.png,"The variable perimeter_se discriminates between the target values, as shown in the decision tree.",12048
+heart_decision_tree.png,"The variable trestbps discriminates between the target values, as shown in the decision tree.",3250
+Wine_decision_tree.png,"The variable Flavanoids discriminates between the target values, as shown in the decision tree.",8790
+Iris_decision_tree.png,"The variable PetalLengthCm discriminates between the target values, as shown in the decision tree.",7867
+Breast_Cancer_decision_tree.png,"The variable texture_worst discriminates between the target values, as shown in the decision tree.",12053
+Churn_Modelling_decision_tree.png,"The variable NumOfProducts discriminates between the target values, as shown in the decision tree.",2138
+heart_decision_tree.png,"The variable cp discriminates between the target values, as shown in the decision tree.",3249
+heart_decision_tree.png,"The variable age discriminates between the target values, as shown in the decision tree.",3248
+vehicle_decision_tree.png,"The variable MAJORVARIANCE discriminates between the target values, as shown in the decision tree.",5516
+BankNoteAuthentication_decision_tree.png,"The variable entropy discriminates between the target values, as shown in the decision tree.",9997
+vehicle_decision_tree.png,"The variable MAJORKURTOSIS discriminates between the target values, as shown in the decision tree.",5522
+Breast_Cancer_decision_tree.png,"The variable perimeter_worst discriminates between the target values, as shown in the decision tree.",12054
+vehicle_decision_tree.png,"The variable PR AXIS ASPECT RATIO discriminates between the target values, as shown in the decision tree.",5510
+diabetes_decision_tree.png,"The variable Insulin discriminates between the target values, as shown in the decision tree.",10668
+diabetes_decision_tree.png,"The variable Age discriminates between the target values, as shown in the decision tree.",10671
+diabetes_decision_tree.png,"The variable SkinThickness discriminates between the target values, as shown in the decision tree.",10667
+Churn_Modelling_decision_tree.png,"The variable CreditScore discriminates between the target values, as shown in the decision tree.",2134
+Breast_Cancer_decision_tree.png,"The variable radius_worst discriminates between the target values, as shown in the decision tree.",12052
+Wine_decision_tree.png,"The variable Color intensity discriminates between the target values, as shown in the decision tree.",8793
+vehicle_decision_tree.png,"The variable ELONGATEDNESS discriminates between the target values, as shown in the decision tree.",5513
+heart_decision_tree.png,"The variable restecg discriminates between the target values, as shown in the decision tree.",3252
+Wine_decision_tree.png,"The variable Class discriminates between the target values, as shown in the decision tree.",8784
+Breast_Cancer_decision_tree.png,"The variable symmetry_se discriminates between the target values, as shown in the decision tree.",12051
+vehicle_decision_tree.png,"The variable CIRCULARITY discriminates between the target values, as shown in the decision tree.",5507
+Breast_Cancer_decision_tree.png,"The variable perimeter_mean discriminates between the target values, as shown in the decision tree.",12046
+diabetes_decision_tree.png,"The variable Glucose discriminates between the target values, as shown in the decision tree.",10665
+vehicle_decision_tree.png,"The variable MINORKURTOSIS discriminates between the target values, as shown in the decision tree.",5521
+Churn_Modelling_decision_tree.png,"The variable Tenure discriminates between the target values, as shown in the decision tree.",2136
+Wine_decision_tree.png,"The variable Nonflavanoid phenols discriminates between the target values, as shown in the decision tree.",8791
+Wine_decision_tree.png,"The variable Total phenols discriminates between the target values, as shown in the decision tree.",8789
+adult_decision_tree.png,"The variable fnlwgt discriminates between the target values, as shown in the decision tree.",1077
+diabetes_decision_tree.png,"The variable DiabetesPedigreeFunction discriminates between the target values, as shown in the decision tree.",10670
+diabetes_decision_tree.png,"The variable BMI discriminates between the target values, as shown in the decision tree.",10669
+BankNoteAuthentication_decision_tree.png,"The variable skewness discriminates between the target values, as shown in the decision tree.",9995
+vehicle_decision_tree.png,"The variable RADIUS RATIO discriminates between the target values, as shown in the decision tree.",5509
+vehicle_decision_tree.png,"The variable DISTANCE CIRCULARITY discriminates between the target values, as shown in the decision tree.",5508
+diabetes_decision_tree.png,"The variable BloodPressure discriminates between the target values, as shown in the decision tree.",10666
+Wine_decision_tree.png,"The variable Proanthocyanins discriminates between the target values, as shown in the decision tree.",8792
+Iris_decision_tree.png,"The variable SepalWidthCm discriminates between the target values, as shown in the decision tree.",7866
+adult_decision_tree.png,"The variable hours-per-week discriminates between the target values, as shown in the decision tree.",1081
+heart_decision_tree.png,"The variable thalach discriminates between the target values, as shown in the decision tree.",3253
+Churn_Modelling_decision_tree.png,"The variable Balance discriminates between the target values, as shown in the decision tree.",2137
+Wine_decision_tree.png,"The variable Alcalinity of ash discriminates between the target values, as shown in the decision tree.",8788
+adult_decision_tree.png,"The variable capital-loss discriminates between the target values, as shown in the decision tree.",1080
+vehicle_decision_tree.png,"The variable MINORSKEWNESS discriminates between the target values, as shown in the decision tree.",5520
+adult_decision_tree.png,"The variable age discriminates between the target values, as shown in the decision tree.",1076
+Wine_decision_tree.png,"The variable Alcohol discriminates between the target values, as shown in the decision tree.",8785
+BankNoteAuthentication_decision_tree.png,"The variable variance discriminates between the target values, as shown in the decision tree.",9994
+vehicle_decision_tree.png,"The variable GYRATIONRADIUS discriminates between the target values, as shown in the decision tree.",5518
+heart_decision_tree.png,"The variable ca discriminates between the target values, as shown in the decision tree.",3256
+Churn_Modelling_decision_tree.png,"The variable EstimatedSalary discriminates between the target values, as shown in the decision tree.",2139
+vehicle_decision_tree.png,"The variable SCATTER RATIO discriminates between the target values, as shown in the decision tree.",5512
+BankNoteAuthentication_decision_tree.png,"The variable curtosis discriminates between the target values, as shown in the decision tree.",9996
+vehicle_decision_tree.png,"The variable HOLLOWS RATIO discriminates between the target values, as shown in the decision tree.",5523
+diabetes_decision_tree.png,"The variable Pregnancies discriminates between the target values, as shown in the decision tree.",10664
+vehicle_decision_tree.png,"The variable PR AXISRECTANGULAR discriminates between the target values, as shown in the decision tree.",5514
+vehicle_decision_tree.png,"The variable target discriminates between the target values, as shown in the decision tree.",5524
+vehicle_decision_tree.png,"The variable LENGTHRECTANGULAR discriminates between the target values, as shown in the decision tree.",5515
+adult_decision_tree.png,"The variable capital-gain discriminates between the target values, as shown in the decision tree.",1079
+Breast_Cancer_decision_tree.png,"The variable smoothness_se discriminates between the target values, as shown in the decision tree.",12050
+Wine_decision_tree.png,"The variable Malic acid discriminates between the target values, as shown in the decision tree.",8786
+Wine_decision_tree.png,"The variable Hue discriminates between the target values, as shown in the decision tree.",8794
+heart_decision_tree.png,"The variable oldpeak discriminates between the target values, as shown in the decision tree.",3254
+vehicle_decision_tree.png,"The variable MAJORSKEWNESS discriminates between the target values, as shown in the decision tree.",5519
+Wine_decision_tree.png,"The variable Ash discriminates between the target values, as shown in the decision tree.",8787
+Breast_Cancer_decision_tree.png,"The variable area_se discriminates between the target values, as shown in the decision tree.",12049
+heart_decision_tree.png,"The variable slope discriminates between the target values, as shown in the decision tree.",3255
+heart_decision_tree.png,"The variable thal discriminates between the target values, as shown in the decision tree.",3257
+vehicle_decision_tree.png,"The variable MAX LENGTH ASPECT RATIO discriminates between the target values, as shown in the decision tree.",5511
+Breast_Cancer_decision_tree.png,"The variable texture_mean discriminates between the target values, as shown in the decision tree.",12045
+Iris_decision_tree.png,"The variable SepalLengthCm discriminates between the target values, as shown in the decision tree.",7865
+adult_decision_tree.png,"The variable educational-num discriminates between the target values, as shown in the decision tree.",1078
+vehicle_decision_tree.png,"The variable MINORVARIANCE discriminates between the target values, as shown in the decision tree.",5517
+Iris_decision_tree.png,It is possible to state that SepalLengthCm is the second most discriminative variable regarding the class.,7861
+vehicle_decision_tree.png,It is possible to state that MAX LENGTH ASPECT RATIO is the second most discriminative variable regarding the class.,5492
+heart_decision_tree.png,It is possible to state that restecg is the first most discriminative variable regarding the class.,3232
+heart_decision_tree.png,It is possible to state that trestbps is the first most discriminative variable regarding the class.,3230
+Churn_Modelling_decision_tree.png,It is possible to state that EstimatedSalary is the first most discriminative variable regarding the class.,2127
+Wine_decision_tree.png,It is possible to state that Color intensity is the second most discriminative variable regarding the class.,8781
+adult_decision_tree.png,It is possible to state that capital-gain is the first most discriminative variable regarding the class.,1067
+diabetes_decision_tree.png,It is possible to state that SkinThickness is the second most discriminative variable regarding the class.,10659
+diabetes_decision_tree.png,It is possible to state that BloodPressure is the second most discriminative variable regarding the class.,10658
+adult_decision_tree.png,It is possible to state that capital-loss is the second most discriminative variable regarding the class.,1074
+heart_decision_tree.png,It is possible to state that thalach is the second most discriminative variable regarding the class.,3243
+heart_decision_tree.png,It is possible to state that cp is the second most discriminative variable regarding the class.,3239
+Wine_decision_tree.png,It is possible to state that Class is the second most discriminative variable regarding the class.,8772
+diabetes_decision_tree.png,It is possible to state that BMI is the first most discriminative variable regarding the class.,10653
+vehicle_decision_tree.png,It is possible to state that HOLLOWS RATIO is the first most discriminative variable regarding the class.,5485
+Breast_Cancer_decision_tree.png,It is possible to state that perimeter_mean is the second most discriminative variable regarding the class.,12036
+diabetes_decision_tree.png,It is possible to state that Glucose is the second most discriminative variable regarding the class.,10657
+Iris_decision_tree.png,It is possible to state that PetalWidthCm is the first most discriminative variable regarding the class.,7860
+Breast_Cancer_decision_tree.png,It is possible to state that perimeter_worst is the first most discriminative variable regarding the class.,12034
+diabetes_decision_tree.png,It is possible to state that BloodPressure is the first most discriminative variable regarding the class.,10650
+vehicle_decision_tree.png,It is possible to state that MAJORSKEWNESS is the first most discriminative variable regarding the class.,5481
+Wine_decision_tree.png,It is possible to state that Ash is the second most discriminative variable regarding the class.,8775
+Wine_decision_tree.png,It is possible to state that Proanthocyanins is the first most discriminative variable regarding the class.,8768
+diabetes_decision_tree.png,It is possible to state that Insulin is the second most discriminative variable regarding the class.,10660
+Churn_Modelling_decision_tree.png,It is possible to state that EstimatedSalary is the second most discriminative variable regarding the class.,2133
+vehicle_decision_tree.png,It is possible to state that GYRATIONRADIUS is the first most discriminative variable regarding the class.,5480
+Breast_Cancer_decision_tree.png,It is possible to state that perimeter_se is the first most discriminative variable regarding the class.,12028
+Wine_decision_tree.png,It is possible to state that Malic acid is the second most discriminative variable regarding the class.,8774
+heart_decision_tree.png,It is possible to state that oldpeak is the first most discriminative variable regarding the class.,3234
+BankNoteAuthentication_decision_tree.png,It is possible to state that skewness is the first most discriminative variable regarding the class.,9987
+Breast_Cancer_decision_tree.png,It is possible to state that area_se is the second most discriminative variable regarding the class.,12039
+heart_decision_tree.png,It is possible to state that restecg is the second most discriminative variable regarding the class.,3242
+BankNoteAuthentication_decision_tree.png,It is possible to state that variance is the first most discriminative variable regarding the class.,9986
+Iris_decision_tree.png,It is possible to state that SepalLengthCm is the first most discriminative variable regarding the class.,7857
+heart_decision_tree.png,It is possible to state that ca is the first most discriminative variable regarding the class.,3236
+Breast_Cancer_decision_tree.png,It is possible to state that symmetry_se is the first most discriminative variable regarding the class.,12031
+heart_decision_tree.png,It is possible to state that thal is the first most discriminative variable regarding the class.,3237
+heart_decision_tree.png,It is possible to state that trestbps is the second most discriminative variable regarding the class.,3240
+Wine_decision_tree.png,It is possible to state that OD280-OD315 of diluted wines is the first most discriminative variable regarding the class.,8771
+diabetes_decision_tree.png,It is possible to state that BMI is the second most discriminative variable regarding the class.,10661
+Breast_Cancer_decision_tree.png,It is possible to state that texture_mean is the first most discriminative variable regarding the class.,12025
+vehicle_decision_tree.png,It is possible to state that MAX LENGTH ASPECT RATIO is the first most discriminative variable regarding the class.,5473
+adult_decision_tree.png,It is possible to state that fnlwgt is the first most discriminative variable regarding the class.,1065
+diabetes_decision_tree.png,It is possible to state that SkinThickness is the first most discriminative variable regarding the class.,10651
+Churn_Modelling_decision_tree.png,It is possible to state that Tenure is the first most discriminative variable regarding the class.,2124
+vehicle_decision_tree.png,It is possible to state that CIRCULARITY is the first most discriminative variable regarding the class.,5469
+heart_decision_tree.png,It is possible to state that oldpeak is the second most discriminative variable regarding the class.,3244
+adult_decision_tree.png,It is possible to state that hours-per-week is the first most discriminative variable regarding the class.,1069
+vehicle_decision_tree.png,It is possible to state that MINORVARIANCE is the first most discriminative variable regarding the class.,5479
+vehicle_decision_tree.png,It is possible to state that MAJORVARIANCE is the second most discriminative variable regarding the class.,5497
+vehicle_decision_tree.png,It is possible to state that COMPACTNESS is the second most discriminative variable regarding the class.,5487
+Churn_Modelling_decision_tree.png,It is possible to state that Age is the second most discriminative variable regarding the class.,2129
+adult_decision_tree.png,It is possible to state that capital-gain is the second most discriminative variable regarding the class.,1073
+Breast_Cancer_decision_tree.png,It is possible to state that texture_worst is the first most discriminative variable regarding the class.,12033
+Iris_decision_tree.png,It is possible to state that SepalWidthCm is the second most discriminative variable regarding the class.,7862
+vehicle_decision_tree.png,It is possible to state that DISTANCE CIRCULARITY is the first most discriminative variable regarding the class.,5470
+vehicle_decision_tree.png,It is possible to state that LENGTHRECTANGULAR is the second most discriminative variable regarding the class.,5496
+vehicle_decision_tree.png,It is possible to state that SCATTER RATIO is the second most discriminative variable regarding the class.,5493
+diabetes_decision_tree.png,It is possible to state that Age is the second most discriminative variable regarding the class.,10663
+heart_decision_tree.png,It is possible to state that thal is the second most discriminative variable regarding the class.,3247
+Churn_Modelling_decision_tree.png,It is possible to state that CreditScore is the first most discriminative variable regarding the class.,2122
+diabetes_decision_tree.png,It is possible to state that DiabetesPedigreeFunction is the second most discriminative variable regarding the class.,10662
+Wine_decision_tree.png,It is possible to state that Alcohol is the first most discriminative variable regarding the class.,8761
+Wine_decision_tree.png,It is possible to state that Alcalinity of ash is the second most discriminative variable regarding the class.,8776
+Wine_decision_tree.png,It is possible to state that Nonflavanoid phenols is the second most discriminative variable regarding the class.,8779
+Iris_decision_tree.png,It is possible to state that SepalWidthCm is the first most discriminative variable regarding the class.,7858
+diabetes_decision_tree.png,It is possible to state that Pregnancies is the first most discriminative variable regarding the class.,10648
+heart_decision_tree.png,It is possible to state that cp is the first most discriminative variable regarding the class.,3229
+Breast_Cancer_decision_tree.png,It is possible to state that radius_worst is the first most discriminative variable regarding the class.,12032
+Wine_decision_tree.png,It is possible to state that Hue is the second most discriminative variable regarding the class.,8782
+adult_decision_tree.png,It is possible to state that capital-loss is the first most discriminative variable regarding the class.,1068
+vehicle_decision_tree.png,It is possible to state that MINORKURTOSIS is the first most discriminative variable regarding the class.,5483
+Wine_decision_tree.png,It is possible to state that Proanthocyanins is the second most discriminative variable regarding the class.,8780
+BankNoteAuthentication_decision_tree.png,It is possible to state that skewness is the second most discriminative variable regarding the class.,9991
+vehicle_decision_tree.png,It is possible to state that MINORSKEWNESS is the second most discriminative variable regarding the class.,5501
+heart_decision_tree.png,It is possible to state that thalach is the first most discriminative variable regarding the class.,3233
+adult_decision_tree.png,It is possible to state that age is the second most discriminative variable regarding the class.,1070
+diabetes_decision_tree.png,It is possible to state that DiabetesPedigreeFunction is the first most discriminative variable regarding the class.,10654
+vehicle_decision_tree.png,It is possible to state that SCATTER RATIO is the first most discriminative variable regarding the class.,5474
+Wine_decision_tree.png,It is possible to state that Total phenols is the first most discriminative variable regarding the class.,8765
+Breast_Cancer_decision_tree.png,It is possible to state that texture_worst is the second most discriminative variable regarding the class.,12043
+diabetes_decision_tree.png,It is possible to state that Age is the first most discriminative variable regarding the class.,10655
+Churn_Modelling_decision_tree.png,It is possible to state that CreditScore is the second most discriminative variable regarding the class.,2128
+Iris_decision_tree.png,It is possible to state that PetalLengthCm is the second most discriminative variable regarding the class.,7863
+Breast_Cancer_decision_tree.png,It is possible to state that perimeter_se is the second most discriminative variable regarding the class.,12038
+heart_decision_tree.png,It is possible to state that slope is the first most discriminative variable regarding the class.,3235
+Iris_decision_tree.png,It is possible to state that PetalLengthCm is the first most discriminative variable regarding the class.,7859
+heart_decision_tree.png,It is possible to state that age is the second most discriminative variable regarding the class.,3238
+Churn_Modelling_decision_tree.png,It is possible to state that NumOfProducts is the second most discriminative variable regarding the class.,2132
+adult_decision_tree.png,It is possible to state that educational-num is the second most discriminative variable regarding the class.,1072
+heart_decision_tree.png,It is possible to state that chol is the second most discriminative variable regarding the class.,3241
+vehicle_decision_tree.png,It is possible to state that CIRCULARITY is the second most discriminative variable regarding the class.,5488
+vehicle_decision_tree.png,It is possible to state that PR AXIS ASPECT RATIO is the first most discriminative variable regarding the class.,5472
+Wine_decision_tree.png,It is possible to state that Malic acid is the first most discriminative variable regarding the class.,8762
+vehicle_decision_tree.png,It is possible to state that ELONGATEDNESS is the second most discriminative variable regarding the class.,5494
+vehicle_decision_tree.png,It is possible to state that RADIUS RATIO is the second most discriminative variable regarding the class.,5490
+vehicle_decision_tree.png,It is possible to state that HOLLOWS RATIO is the second most discriminative variable regarding the class.,5504
+vehicle_decision_tree.png,It is possible to state that LENGTHRECTANGULAR is the first most discriminative variable regarding the class.,5477
+Wine_decision_tree.png,It is possible to state that Flavanoids is the second most discriminative variable regarding the class.,8778
+BankNoteAuthentication_decision_tree.png,It is possible to state that curtosis is the first most discriminative variable regarding the class.,9988
+Wine_decision_tree.png,It is possible to state that Alcohol is the second most discriminative variable regarding the class.,8773
+Churn_Modelling_decision_tree.png,It is possible to state that Age is the first most discriminative variable regarding the class.,2123
+vehicle_decision_tree.png,It is possible to state that MAJORKURTOSIS is the second most discriminative variable regarding the class.,5503
+Wine_decision_tree.png,It is possible to state that Ash is the first most discriminative variable regarding the class.,8763
+vehicle_decision_tree.png,It is possible to state that target is the second most discriminative variable regarding the class.,5505
+Wine_decision_tree.png,It is possible to state that Hue is the first most discriminative variable regarding the class.,8770
+BankNoteAuthentication_decision_tree.png,It is possible to state that curtosis is the second most discriminative variable regarding the class.,9992
+Wine_decision_tree.png,It is possible to state that Class is the first most discriminative variable regarding the class.,8760
+heart_decision_tree.png,It is possible to state that ca is the second most discriminative variable regarding the class.,3246
+Wine_decision_tree.png,It is possible to state that Flavanoids is the first most discriminative variable regarding the class.,8766
+Breast_Cancer_decision_tree.png,It is possible to state that perimeter_mean is the first most discriminative variable regarding the class.,12026
+Churn_Modelling_decision_tree.png,It is possible to state that Balance is the first most discriminative variable regarding the class.,2125
+adult_decision_tree.png,It is possible to state that hours-per-week is the second most discriminative variable regarding the class.,1075
+BankNoteAuthentication_decision_tree.png,It is possible to state that entropy is the second most discriminative variable regarding the class.,9993
+diabetes_decision_tree.png,It is possible to state that Pregnancies is the second most discriminative variable regarding the class.,10656
+Churn_Modelling_decision_tree.png,It is possible to state that Balance is the second most discriminative variable regarding the class.,2131
+Breast_Cancer_decision_tree.png,It is possible to state that smoothness_se is the first most discriminative variable regarding the class.,12030
+Breast_Cancer_decision_tree.png,It is possible to state that symmetry_se is the second most discriminative variable regarding the class.,12041
+vehicle_decision_tree.png,It is possible to state that DISTANCE CIRCULARITY is the second most discriminative variable regarding the class.,5489
+Breast_Cancer_decision_tree.png,It is possible to state that perimeter_worst is the second most discriminative variable regarding the class.,12044
+heart_decision_tree.png,It is possible to state that age is the first most discriminative variable regarding the class.,3228
+adult_decision_tree.png,It is possible to state that age is the first most discriminative variable regarding the class.,1064
+Wine_decision_tree.png,It is possible to state that Color intensity is the first most discriminative variable regarding the class.,8769
+Breast_Cancer_decision_tree.png,It is possible to state that texture_mean is the second most discriminative variable regarding the class.,12035
+Churn_Modelling_decision_tree.png,It is possible to state that Tenure is the second most discriminative variable regarding the class.,2130
+vehicle_decision_tree.png,It is possible to state that MAJORKURTOSIS is the first most discriminative variable regarding the class.,5484
+Breast_Cancer_decision_tree.png,It is possible to state that smoothness_se is the second most discriminative variable regarding the class.,12040
+vehicle_decision_tree.png,It is possible to state that MAJORSKEWNESS is the second most discriminative variable regarding the class.,5500
+diabetes_decision_tree.png,It is possible to state that Insulin is the first most discriminative variable regarding the class.,10652
+Breast_Cancer_decision_tree.png,It is possible to state that texture_se is the second most discriminative variable regarding the class.,12037
+adult_decision_tree.png,It is possible to state that fnlwgt is the second most discriminative variable regarding the class.,1071
+BankNoteAuthentication_decision_tree.png,It is possible to state that entropy is the first most discriminative variable regarding the class.,9989
+vehicle_decision_tree.png,It is possible to state that COMPACTNESS is the first most discriminative variable regarding the class.,5468
+Breast_Cancer_decision_tree.png,It is possible to state that texture_se is the first most discriminative variable regarding the class.,12027
+vehicle_decision_tree.png,It is possible to state that PR AXIS ASPECT RATIO is the second most discriminative variable regarding the class.,5491
+Breast_Cancer_decision_tree.png,It is possible to state that area_se is the first most discriminative variable regarding the class.,12029
+BankNoteAuthentication_decision_tree.png,It is possible to state that variance is the second most discriminative variable regarding the class.,9990
+vehicle_decision_tree.png,It is possible to state that ELONGATEDNESS is the first most discriminative variable regarding the class.,5475
+vehicle_decision_tree.png,It is possible to state that GYRATIONRADIUS is the second most discriminative variable regarding the class.,5499
+heart_decision_tree.png,It is possible to state that chol is the first most discriminative variable regarding the class.,3231
+vehicle_decision_tree.png,It is possible to state that RADIUS RATIO is the first most discriminative variable regarding the class.,5471
+vehicle_decision_tree.png,It is possible to state that PR AXISRECTANGULAR is the first most discriminative variable regarding the class.,5476
+heart_decision_tree.png,It is possible to state that slope is the second most discriminative variable regarding the class.,3245
+adult_decision_tree.png,It is possible to state that educational-num is the first most discriminative variable regarding the class.,1066
+Wine_decision_tree.png,It is possible to state that OD280-OD315 of diluted wines is the second most discriminative variable regarding the class.,8783
+vehicle_decision_tree.png,It is possible to state that MAJORVARIANCE is the first most discriminative variable regarding the class.,5478
+Wine_decision_tree.png,It is possible to state that Alcalinity of ash is the first most discriminative variable regarding the class.,8764
+Wine_decision_tree.png,It is possible to state that Total phenols is the second most discriminative variable regarding the class.,8777
+Wine_decision_tree.png,It is possible to state that Nonflavanoid phenols is the first most discriminative variable regarding the class.,8767
+diabetes_decision_tree.png,It is possible to state that Glucose is the first most discriminative variable regarding the class.,10649
+vehicle_decision_tree.png,It is possible to state that MINORVARIANCE is the second most discriminative variable regarding the class.,5498
+Breast_Cancer_decision_tree.png,It is possible to state that radius_worst is the second most discriminative variable regarding the class.,12042
+vehicle_decision_tree.png,It is possible to state that PR AXISRECTANGULAR is the second most discriminative variable regarding the class.,5495
+Iris_decision_tree.png,It is possible to state that PetalWidthCm is the second most discriminative variable regarding the class.,7864
+Churn_Modelling_decision_tree.png,It is possible to state that NumOfProducts is the first most discriminative variable regarding the class.,2126
+vehicle_decision_tree.png,It is possible to state that MINORKURTOSIS is the second most discriminative variable regarding the class.,5502
+vehicle_decision_tree.png,It is possible to state that target is the first most discriminative variable regarding the class.,5486
+vehicle_decision_tree.png,It is possible to state that MINORSKEWNESS is the first most discriminative variable regarding the class.,5482
+Wine_decision_tree.png,Variable OD280-OD315 of diluted wines is one of the most relevant variables.,8759
+vehicle_decision_tree.png,Variable LENGTHRECTANGULAR is one of the most relevant variables.,5458
+heart_decision_tree.png,Variable oldpeak is one of the most relevant variables.,3224
+vehicle_decision_tree.png,Variable MAJORVARIANCE is one of the most relevant variables.,5459
+vehicle_decision_tree.png,Variable ELONGATEDNESS is one of the most relevant variables.,5456
+Wine_decision_tree.png,Variable Ash is one of the most relevant variables.,8751
+heart_decision_tree.png,Variable cp is one of the most relevant variables.,3219
+vehicle_decision_tree.png,Variable MAJORSKEWNESS is one of the most relevant variables.,5462
+heart_decision_tree.png,Variable thal is one of the most relevant variables.,3227
+Breast_Cancer_decision_tree.png,Variable perimeter_mean is one of the most relevant variables.,12016
+diabetes_decision_tree.png,Variable Glucose is one of the most relevant variables.,10641
+Wine_decision_tree.png,Variable Class is one of the most relevant variables.,8748
+diabetes_decision_tree.png,Variable BMI is one of the most relevant variables.,10645
+vehicle_decision_tree.png,Variable MINORVARIANCE is one of the most relevant variables.,5460
+Wine_decision_tree.png,Variable Flavanoids is one of the most relevant variables.,8754
+vehicle_decision_tree.png,Variable PR AXIS ASPECT RATIO is one of the most relevant variables.,5453
+vehicle_decision_tree.png,Variable MINORKURTOSIS is one of the most relevant variables.,5464
+vehicle_decision_tree.png,Variable MINORSKEWNESS is one of the most relevant variables.,5463
+diabetes_decision_tree.png,Variable BloodPressure is one of the most relevant variables.,10642
+Breast_Cancer_decision_tree.png,Variable symmetry_se is one of the most relevant variables.,12021
+Churn_Modelling_decision_tree.png,Variable NumOfProducts is one of the most relevant variables.,2120
+diabetes_decision_tree.png,Variable SkinThickness is one of the most relevant variables.,10643
+Wine_decision_tree.png,Variable Total phenols is one of the most relevant variables.,8753
+Iris_decision_tree.png,Variable SepalLengthCm is one of the most relevant variables.,7853
+BankNoteAuthentication_decision_tree.png,Variable entropy is one of the most relevant variables.,9985
+vehicle_decision_tree.png,Variable CIRCULARITY is one of the most relevant variables.,5450
+vehicle_decision_tree.png,Variable COMPACTNESS is one of the most relevant variables.,5449
+Breast_Cancer_decision_tree.png,Variable perimeter_worst is one of the most relevant variables.,12024
+Breast_Cancer_decision_tree.png,Variable area_se is one of the most relevant variables.,12019
+Wine_decision_tree.png,Variable Color intensity is one of the most relevant variables.,8757
+vehicle_decision_tree.png,Variable MAX LENGTH ASPECT RATIO is one of the most relevant variables.,5454
+Wine_decision_tree.png,Variable Hue is one of the most relevant variables.,8758
+adult_decision_tree.png,Variable hours-per-week is one of the most relevant variables.,1063
+BankNoteAuthentication_decision_tree.png,Variable curtosis is one of the most relevant variables.,9984
+diabetes_decision_tree.png,Variable Age is one of the most relevant variables.,10647
+Breast_Cancer_decision_tree.png,Variable texture_worst is one of the most relevant variables.,12023
+Churn_Modelling_decision_tree.png,Variable CreditScore is one of the most relevant variables.,2116
+adult_decision_tree.png,Variable capital-gain is one of the most relevant variables.,1061
+heart_decision_tree.png,Variable trestbps is one of the most relevant variables.,3220
+adult_decision_tree.png,Variable age is one of the most relevant variables.,1058
+BankNoteAuthentication_decision_tree.png,Variable variance is one of the most relevant variables.,9982
+Churn_Modelling_decision_tree.png,Variable EstimatedSalary is one of the most relevant variables.,2121
+heart_decision_tree.png,Variable slope is one of the most relevant variables.,3225
+diabetes_decision_tree.png,Variable Insulin is one of the most relevant variables.,10644
+diabetes_decision_tree.png,Variable Pregnancies is one of the most relevant variables.,10640
+heart_decision_tree.png,Variable age is one of the most relevant variables.,3218
+heart_decision_tree.png,Variable ca is one of the most relevant variables.,3226
+Breast_Cancer_decision_tree.png,Variable smoothness_se is one of the most relevant variables.,12020
+Wine_decision_tree.png,Variable Alcalinity of ash is one of the most relevant variables.,8752
+vehicle_decision_tree.png,Variable SCATTER RATIO is one of the most relevant variables.,5455
+heart_decision_tree.png,Variable thalach is one of the most relevant variables.,3223
+vehicle_decision_tree.png,Variable DISTANCE CIRCULARITY is one of the most relevant variables.,5451
+vehicle_decision_tree.png,Variable MAJORKURTOSIS is one of the most relevant variables.,5465
+Breast_Cancer_decision_tree.png,Variable texture_se is one of the most relevant variables.,12017
+Wine_decision_tree.png,Variable Nonflavanoid phenols is one of the most relevant variables.,8755
+Iris_decision_tree.png,Variable PetalLengthCm is one of the most relevant variables.,7855
+diabetes_decision_tree.png,Variable DiabetesPedigreeFunction is one of the most relevant variables.,10646
+Iris_decision_tree.png,Variable SepalWidthCm is one of the most relevant variables.,7854
+vehicle_decision_tree.png,Variable RADIUS RATIO is one of the most relevant variables.,5452
+Wine_decision_tree.png,Variable Malic acid is one of the most relevant variables.,8750
+Wine_decision_tree.png,Variable Alcohol is one of the most relevant variables.,8749
+Breast_Cancer_decision_tree.png,Variable perimeter_se is one of the most relevant variables.,12018
+vehicle_decision_tree.png,Variable HOLLOWS RATIO is one of the most relevant variables.,5466
+vehicle_decision_tree.png,Variable GYRATIONRADIUS is one of the most relevant variables.,5461
+adult_decision_tree.png,Variable educational-num is one of the most relevant variables.,1060
+BankNoteAuthentication_decision_tree.png,Variable skewness is one of the most relevant variables.,9983
+vehicle_decision_tree.png,Variable PR AXISRECTANGULAR is one of the most relevant variables.,5457
+Breast_Cancer_decision_tree.png,Variable texture_mean is one of the most relevant variables.,12015
+heart_decision_tree.png,Variable chol is one of the most relevant variables.,3221
+adult_decision_tree.png,Variable capital-loss is one of the most relevant variables.,1062
+Churn_Modelling_decision_tree.png,Variable Tenure is one of the most relevant variables.,2118
+Breast_Cancer_decision_tree.png,Variable radius_worst is one of the most relevant variables.,12022
+heart_decision_tree.png,Variable restecg is one of the most relevant variables.,3222
+vehicle_decision_tree.png,Variable target is one of the most relevant variables.,5467
+adult_decision_tree.png,Variable fnlwgt is one of the most relevant variables.,1059
+Churn_Modelling_decision_tree.png,Variable Balance is one of the most relevant variables.,2119
+Churn_Modelling_decision_tree.png,Variable Age is one of the most relevant variables.,2117
+Wine_decision_tree.png,Variable Proanthocyanins is one of the most relevant variables.,8756
+Iris_decision_tree.png,Variable PetalWidthCm is one of the most relevant variables.,7856
+vehicle_correlation_heatmap.png,Variable PR AXIS ASPECT RATIO seems to be relevant for the majority of mining tasks.,5434
+Wine_correlation_heatmap.png,Variable Flavanoids seems to be relevant for the majority of mining tasks.,8742
+Breast_Cancer_correlation_heatmap.png,Variable perimeter_worst seems to be relevant for the majority of mining tasks.,12014
+Iris_correlation_heatmap.png,Variable SepalWidthCm seems to be relevant for the majority of mining tasks.,7850
+vehicle_correlation_heatmap.png,Variable MINORKURTOSIS seems to be relevant for the majority of mining tasks.,5445
+heart_correlation_heatmap.png,Variable cp seems to be relevant for the majority of mining tasks.,3209
+vehicle_correlation_heatmap.png,Variable GYRATIONRADIUS seems to be relevant for the majority of mining tasks.,5442
+adult_correlation_heatmap.png,Variable educational-num seems to be relevant for the majority of mining tasks.,1054
+Wine_correlation_heatmap.png,Variable Malic acid seems to be relevant for the majority of mining tasks.,8738
+Breast_Cancer_correlation_heatmap.png,Variable area_se seems to be relevant for the majority of mining tasks.,12009
+Breast_Cancer_correlation_heatmap.png,Variable symmetry_se seems to be relevant for the majority of mining tasks.,12011
+BankNoteAuthentication_correlation_heatmap.png,Variable skewness seems to be relevant for the majority of mining tasks.,9979
+heart_correlation_heatmap.png,Variable thalach seems to be relevant for the majority of mining tasks.,3213
+Breast_Cancer_correlation_heatmap.png,Variable perimeter_mean seems to be relevant for the majority of mining tasks.,12006
+vehicle_correlation_heatmap.png,Variable DISTANCE CIRCULARITY seems to be relevant for the majority of mining tasks.,5432
+vehicle_correlation_heatmap.png,Variable MAJORSKEWNESS seems to be relevant for the majority of mining tasks.,5443
+Churn_Modelling_correlation_heatmap.png,Variable Tenure seems to be relevant for the majority of mining tasks.,2112
+vehicle_correlation_heatmap.png,Variable MAJORKURTOSIS seems to be relevant for the majority of mining tasks.,5446
+diabetes_correlation_heatmap.png,Variable DiabetesPedigreeFunction seems to be relevant for the majority of mining tasks.,10638
+BankNoteAuthentication_correlation_heatmap.png,Variable curtosis seems to be relevant for the majority of mining tasks.,9980
+diabetes_correlation_heatmap.png,Variable Insulin seems to be relevant for the majority of mining tasks.,10636
+Wine_correlation_heatmap.png,Variable Nonflavanoid phenols seems to be relevant for the majority of mining tasks.,8743
+vehicle_correlation_heatmap.png,Variable LENGTHRECTANGULAR seems to be relevant for the majority of mining tasks.,5439
+Churn_Modelling_correlation_heatmap.png,Variable CreditScore seems to be relevant for the majority of mining tasks.,2110
+diabetes_correlation_heatmap.png,Variable Pregnancies seems to be relevant for the majority of mining tasks.,10632
+Wine_correlation_heatmap.png,Variable Ash seems to be relevant for the majority of mining tasks.,8739
+vehicle_correlation_heatmap.png,Variable HOLLOWS RATIO seems to be relevant for the majority of mining tasks.,5447
+Wine_correlation_heatmap.png,Variable Color intensity seems to be relevant for the majority of mining tasks.,8745
+vehicle_correlation_heatmap.png,Variable MINORSKEWNESS seems to be relevant for the majority of mining tasks.,5444
+vehicle_correlation_heatmap.png,Variable MAJORVARIANCE seems to be relevant for the majority of mining tasks.,5440
+Breast_Cancer_correlation_heatmap.png,Variable radius_worst seems to be relevant for the majority of mining tasks.,12012
+heart_correlation_heatmap.png,Variable chol seems to be relevant for the majority of mining tasks.,3211
+Wine_correlation_heatmap.png,Variable Alcohol seems to be relevant for the majority of mining tasks.,8737
+BankNoteAuthentication_correlation_heatmap.png,Variable variance seems to be relevant for the majority of mining tasks.,9978
+Breast_Cancer_correlation_heatmap.png,Variable perimeter_se seems to be relevant for the majority of mining tasks.,12008
+diabetes_correlation_heatmap.png,Variable Age seems to be relevant for the majority of mining tasks.,10639
+adult_correlation_heatmap.png,Variable hours-per-week seems to be relevant for the majority of mining tasks.,1057
+heart_correlation_heatmap.png,Variable restecg seems to be relevant for the majority of mining tasks.,3212
+BankNoteAuthentication_correlation_heatmap.png,Variable entropy seems to be relevant for the majority of mining tasks.,9981
+Iris_correlation_heatmap.png,Variable PetalLengthCm seems to be relevant for the majority of mining tasks.,7851
+adult_correlation_heatmap.png,Variable fnlwgt seems to be relevant for the majority of mining tasks.,1053
+vehicle_correlation_heatmap.png,Variable target seems to be relevant for the majority of mining tasks.,5448
+vehicle_correlation_heatmap.png,Variable COMPACTNESS seems to be relevant for the majority of mining tasks.,5430
+adult_correlation_heatmap.png,Variable age seems to be relevant for the majority of mining tasks.,1052
+Wine_correlation_heatmap.png,Variable Total phenols seems to be relevant for the majority of mining tasks.,8741
+vehicle_correlation_heatmap.png,Variable PR AXISRECTANGULAR seems to be relevant for the majority of mining tasks.,5438
+vehicle_correlation_heatmap.png,Variable RADIUS RATIO seems to be relevant for the majority of mining tasks.,5433
+Wine_correlation_heatmap.png,Variable Alcalinity of ash seems to be relevant for the majority of mining tasks.,8740
+Wine_correlation_heatmap.png,Variable OD280-OD315 of diluted wines seems to be relevant for the majority of mining tasks.,8747
+Breast_Cancer_correlation_heatmap.png,Variable smoothness_se seems to be relevant for the majority of mining tasks.,12010
+vehicle_correlation_heatmap.png,Variable SCATTER RATIO seems to be relevant for the majority of mining tasks.,5436
+vehicle_correlation_heatmap.png,Variable ELONGATEDNESS seems to be relevant for the majority of mining tasks.,5437
+Churn_Modelling_correlation_heatmap.png,Variable EstimatedSalary seems to be relevant for the majority of mining tasks.,2115
+Wine_correlation_heatmap.png,Variable Class seems to be relevant for the majority of mining tasks.,8736
+vehicle_correlation_heatmap.png,Variable MINORVARIANCE seems to be relevant for the majority of mining tasks.,5441
+heart_correlation_heatmap.png,Variable slope seems to be relevant for the majority of mining tasks.,3215
+Iris_correlation_heatmap.png,Variable SepalLengthCm seems to be relevant for the majority of mining tasks.,7849
+diabetes_correlation_heatmap.png,Variable SkinThickness seems to be relevant for the majority of mining tasks.,10635
+diabetes_correlation_heatmap.png,Variable Glucose seems to be relevant for the majority of mining tasks.,10633
+Iris_correlation_heatmap.png,Variable PetalWidthCm seems to be relevant for the majority of mining tasks.,7852
+heart_correlation_heatmap.png,Variable age seems to be relevant for the majority of mining tasks.,3208
+diabetes_correlation_heatmap.png,Variable BMI seems to be relevant for the majority of mining tasks.,10637
+vehicle_correlation_heatmap.png,Variable MAX LENGTH ASPECT RATIO seems to be relevant for the majority of mining tasks.,5435
+Wine_correlation_heatmap.png,Variable Proanthocyanins seems to be relevant for the majority of mining tasks.,8744
+adult_correlation_heatmap.png,Variable capital-gain seems to be relevant for the majority of mining tasks.,1055
+Churn_Modelling_correlation_heatmap.png,Variable NumOfProducts seems to be relevant for the majority of mining tasks.,2114
+Breast_Cancer_correlation_heatmap.png,Variable texture_worst seems to be relevant for the majority of mining tasks.,12013
+Breast_Cancer_correlation_heatmap.png,Variable texture_se seems to be relevant for the majority of mining tasks.,12007
+heart_correlation_heatmap.png,Variable trestbps seems to be relevant for the majority of mining tasks.,3210
+Wine_correlation_heatmap.png,Variable Hue seems to be relevant for the majority of mining tasks.,8746
+heart_correlation_heatmap.png,Variable thal seems to be relevant for the majority of mining tasks.,3217
+heart_correlation_heatmap.png,Variable ca seems to be relevant for the majority of mining tasks.,3216
+diabetes_correlation_heatmap.png,Variable BloodPressure seems to be relevant for the majority of mining tasks.,10634
+heart_correlation_heatmap.png,Variable oldpeak seems to be relevant for the majority of mining tasks.,3214
+Churn_Modelling_correlation_heatmap.png,Variable Age seems to be relevant for the majority of mining tasks.,2111
+adult_correlation_heatmap.png,Variable capital-loss seems to be relevant for the majority of mining tasks.,1056
+Churn_Modelling_correlation_heatmap.png,Variable Balance seems to be relevant for the majority of mining tasks.,2113
+Breast_Cancer_correlation_heatmap.png,Variable texture_mean seems to be relevant for the majority of mining tasks.,12005
+vehicle_correlation_heatmap.png,Variable CIRCULARITY seems to be relevant for the majority of mining tasks.,5431
+BankNoteAuthentication_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",9964
+Wine_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",8603
+Wine_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",8602
+diabetes_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",10574
+heart_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",3127
+vehicle_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",5088
+Churn_Modelling_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",2079
+adult_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",1020
+BankNoteAuthentication_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",9965
+Churn_Modelling_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",2078
+diabetes_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",10575
+Breast_Cancer_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",11913
+adult_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",1021
+vehicle_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",5087
+Breast_Cancer_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",11914
+heart_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",3126
+Iris_boxplots.png,"A scaling transformation is mandatory, in order to improve the  KNN performance in this dataset.",7836
+Iris_boxplots.png,"A scaling transformation is mandatory, in order to improve the Naive Bayes performance in this dataset.",7835
+diabetes_histograms.png,"Considering the common semantics for Insulin and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10524
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4821
+adult_histograms.png,"Considering the common semantics for capital-gain and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",905
+vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5010
+Wine_histograms.png,"Considering the common semantics for Flavanoids and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8573
+Churn_Modelling_histograms.png,"Considering the common semantics for Gender and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2049
+adult_histograms.png,"Considering the common semantics for relationship and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",975
+Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2056
+vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4883
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11890
+Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8574
+vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4798
+diabetes_histograms.png,"Considering the common semantics for Age and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10527
+heart_histograms.png,"Considering the common semantics for thalach and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2997
+Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8577
+vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4893
+Wine_histograms.png,"Considering the common semantics for Total phenols and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8592
+vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4839
+adult_histograms.png,"Considering the common semantics for race and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",951
+heart_histograms.png,"Considering the common semantics for cp and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3101
+adult_histograms.png,"Considering the common semantics for capital-gain and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",870
+Churn_Modelling_histograms.png,"Considering the common semantics for Balance and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1997
+vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5029
+adult_histograms.png,"Considering the common semantics for gender and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",952
+Wine_histograms.png,"Considering the common semantics for Ash and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8570
+Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11907
+vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4876
+vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4975
+Churn_Modelling_histograms.png,"Considering the common semantics for Age and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1986
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11884
+heart_histograms.png,"Considering the common semantics for age and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2991
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4950
+heart_histograms.png,"Considering the common semantics for restecg and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3117
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11883
+heart_histograms.png,"Considering the common semantics for chol and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3103
+Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11824
+Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2029
+heart_histograms.png,"Considering the common semantics for trestbps and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3114
+Iris_histograms.png,"Considering the common semantics for PetalWidthCm and SepalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7821
+vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5070
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4811
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4940
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4888
+adult_histograms.png,"Considering the common semantics for race and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1012
+vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4943
+Wine_histograms.png,"Considering the common semantics for Flavanoids and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8563
+diabetes_histograms.png,"Considering the common semantics for Glucose and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10564
+Wine_histograms.png,"Considering the common semantics for Hue and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8506
+Wine_histograms.png,"Considering the common semantics for Color intensity and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8505
+Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and CreditScore variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1991
+vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4932
+heart_histograms.png,"Considering the common semantics for sex and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3112
+adult_histograms.png,"Considering the common semantics for marital-status and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1009
+Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8510
+adult_histograms.png,"Considering the common semantics for education and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",875
+vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4921
+Wine_histograms.png,"Considering the common semantics for Hue and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8576
+Wine_histograms.png,"Considering the common semantics for Malic acid and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8509
+adult_histograms.png,"Considering the common semantics for capital-gain and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",882
+adult_histograms.png,"Considering the common semantics for occupation and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",901
+BankNoteAuthentication_histograms.png,"Considering the common semantics for curtosis and entropy variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9959
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5001
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5018
+diabetes_histograms.png,"Considering the common semantics for Pregnancies and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10556
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4902
+adult_histograms.png,"Considering the common semantics for educational-num and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",996
+adult_histograms.png,"Considering the common semantics for relationship and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",914
+vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5047
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4788
+adult_histograms.png,"Considering the common semantics for occupation and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",913
+adult_histograms.png,"Considering the common semantics for age and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1004
+heart_histograms.png,"Considering the common semantics for fbs and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2995
+vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4832
+heart_histograms.png,"Considering the common semantics for age and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3039
+adult_histograms.png,"Considering the common semantics for capital-gain and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",977
+Iris_histograms.png,"Considering the common semantics for PetalWidthCm and PetalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7827
+vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4781
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5015
+adult_histograms.png,"Considering the common semantics for hours-per-week and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",931
+vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4808
+adult_histograms.png,"Considering the common semantics for hours-per-week and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",991
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4845
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11876
+Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1999
+Wine_histograms.png,"Considering the common semantics for Hue and OD280-OD315 of diluted wines variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8597
+Wine_histograms.png,"Considering the common semantics for Malic acid and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8488
+Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8527
+vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4944
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11847
+vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4925
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5076
+heart_histograms.png,"Considering the common semantics for chol and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3091
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and texture_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11891
+Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8500
+Churn_Modelling_histograms.png,"Considering the common semantics for Gender and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2031
+adult_histograms.png,"Considering the common semantics for age and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",968
+Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2036
+diabetes_histograms.png,"Considering the common semantics for BMI and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10568
+adult_histograms.png,"Considering the common semantics for race and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",892
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4970
+Wine_histograms.png,"Considering the common semantics for Alcohol and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8498
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4967
+vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4884
+Wine_histograms.png,"Considering the common semantics for Malic acid and Flavanoids variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8539
+heart_histograms.png,"Considering the common semantics for fbs and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3031
+adult_histograms.png,"Considering the common semantics for education and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",887
+adult_histograms.png,"Considering the common semantics for age and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",980
+adult_histograms.png,"Considering the common semantics for capital-loss and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",895
+vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4946
+diabetes_histograms.png,"Considering the common semantics for SkinThickness and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10545
+adult_histograms.png,"Considering the common semantics for capital-gain and hours-per-week variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1014
+diabetes_histograms.png,"Considering the common semantics for BloodPressure and SkinThickness variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10537
+heart_histograms.png,"Considering the common semantics for oldpeak and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3119
+Wine_histograms.png,"Considering the common semantics for Color intensity and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8586
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4981
+Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2026
+heart_histograms.png,"Considering the common semantics for restecg and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3093
+adult_histograms.png,"Considering the common semantics for capital-gain and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",917
+heart_histograms.png,"Considering the common semantics for exang and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3070
+heart_histograms.png,"Considering the common semantics for ca and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3085
+vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4929
+diabetes_histograms.png,"Considering the common semantics for Pregnancies and BloodPressure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10528
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5042
+Churn_Modelling_histograms.png,"Considering the common semantics for Tenure and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2042
+Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2017
+Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11861
+vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4955
+heart_histograms.png,"Considering the common semantics for thal and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3050
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11872
+vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5036
+heart_histograms.png,"Considering the common semantics for chol and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2994
+Iris_histograms.png,"Considering the common semantics for SepalLengthCm and SepalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7822
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11864
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4913
+adult_histograms.png,"Considering the common semantics for fnlwgt and capital-loss variables, dummification if applied would increase the risk of facing the curse of dimensionality.",994
+Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2046
+Iris_histograms.png,"Considering the common semantics for PetalLengthCm and SepalLengthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7820
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4957
+heart_histograms.png,"Considering the common semantics for ca and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3001
+vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4791
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and GYRATIONRADIUS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4991
+adult_histograms.png,"Considering the common semantics for relationship and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",891
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and texture_mean variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11819
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11871
+adult_histograms.png,"Considering the common semantics for marital-status and relationship variables, dummification if applied would increase the risk of facing the curse of dimensionality.",949
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and area_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11856
+heart_histograms.png,"Considering the common semantics for trestbps and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2993
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean and smoothness_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11865
+Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8561
+vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5007
+vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4873
+heart_histograms.png,"Considering the common semantics for fbs and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2983
+heart_histograms.png,"Considering the common semantics for slope and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2988
+heart_histograms.png,"Considering the common semantics for ca and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3037
+heart_histograms.png,"Considering the common semantics for thalach and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3106
+heart_histograms.png,"Considering the common semantics for exang and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3046
+heart_histograms.png,"Considering the common semantics for exang and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2986
+BankNoteAuthentication_histograms.png,"Considering the common semantics for skewness and entropy variables, dummification if applied would increase the risk of facing the curse of dimensionality.",9958
+vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5063
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5066
+vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4875
+heart_histograms.png,"Considering the common semantics for slope and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3000
+vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4849
+Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8504
+heart_histograms.png,"Considering the common semantics for age and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3087
+Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2018
+vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5027
+vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4784
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5004
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_se and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11875
+diabetes_histograms.png,"Considering the common semantics for Pregnancies and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10563
+heart_histograms.png,"Considering the common semantics for sex and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3040
+adult_histograms.png,"Considering the common semantics for age and marital-status variables, dummification if applied would increase the risk of facing the curse of dimensionality.",920
+vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5023
+Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11850
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5038
+Churn_Modelling_histograms.png,"Considering the common semantics for Geography and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2048
+vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4843
+heart_histograms.png,"Considering the common semantics for exang and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3034
+heart_histograms.png,"Considering the common semantics for sex and trestbps variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3016
+heart_histograms.png,"Considering the common semantics for thal and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3038
+adult_histograms.png,"Considering the common semantics for workclass and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",898
+heart_histograms.png,"Considering the common semantics for ca and thal variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3121
+Churn_Modelling_histograms.png,"Considering the common semantics for Age and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2023
+adult_histograms.png,"Considering the common semantics for race and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",988
+Wine_histograms.png,"Considering the common semantics for Malic acid and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8569
+heart_histograms.png,"Considering the common semantics for age and cp variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3003
+adult_histograms.png,"Considering the common semantics for workclass and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",981
+adult_histograms.png,"Considering the common semantics for race and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",880
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5055
+heart_histograms.png,"Considering the common semantics for oldpeak and chol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3035
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5008
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4803
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4855
+heart_histograms.png,"Considering the common semantics for sex and exang variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3076
+adult_histograms.png,"Considering the common semantics for relationship and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",867
+vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4835
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11900
+adult_histograms.png,"Considering the common semantics for hours-per-week and race variables, dummification if applied would increase the risk of facing the curse of dimensionality.",967
+Breast_Cancer_histograms.png,"Considering the common semantics for area_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11886
+Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2055
+Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2038
+adult_histograms.png,"Considering the common semantics for marital-status and gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",973
+diabetes_histograms.png,"Considering the common semantics for Pregnancies and Glucose variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10521
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11882
+adult_histograms.png,"Considering the common semantics for capital-loss and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",883
+Breast_Cancer_histograms.png,"Considering the common semantics for area_se and symmetry_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11877
+Churn_Modelling_histograms.png,"Considering the common semantics for Age and Geography variables, dummification if applied would increase the risk of facing the curse of dimensionality.",1995
+heart_histograms.png,"Considering the common semantics for chol and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3055
+Churn_Modelling_histograms.png,"Considering the common semantics for Balance and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2052
+vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and MAJORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5011
+adult_histograms.png,"Considering the common semantics for fnlwgt and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",910
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and HOLLOWS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5077
+heart_histograms.png,"Considering the common semantics for restecg and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2996
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5035
+Wine_histograms.png,"Considering the common semantics for Color intensity and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8495
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5049
+adult_histograms.png,"Considering the common semantics for educational-num and fnlwgt variables, dummification if applied would increase the risk of facing the curse of dimensionality.",888
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4899
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4953
+diabetes_histograms.png,"Considering the common semantics for BloodPressure and DiabetesPedigreeFunction variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10558
+Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2053
+heart_histograms.png,"Considering the common semantics for exang and oldpeak variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3095
+vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4978
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4865
+adult_histograms.png,"Considering the common semantics for education and educational-num variables, dummification if applied would increase the risk of facing the curse of dimensionality.",911
+Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard and IsActiveMember variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2063
+vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and MINORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5040
+Wine_histograms.png,"Considering the common semantics for Alcalinity of ash and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8531
+Churn_Modelling_histograms.png,"Considering the common semantics for Age and NumOfProducts variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2041
+adult_histograms.png,"Considering the common semantics for gender and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",940
+vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4894
+vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS and SCATTER RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4885
+vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5058
+Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11888
+Wine_histograms.png,"Considering the common semantics for Proanthocyanins and Alcohol variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8494
+Wine_histograms.png,"Considering the common semantics for Flavanoids and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8502
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and texture_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11844
+vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4934
+heart_histograms.png,"Considering the common semantics for chol and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3043
+Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11905
+vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and MINORSKEWNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5024
+Wine_histograms.png,"Considering the common semantics for Hue and Total phenols variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8536
+vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS and LENGTHRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4941
+Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2007
+vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4903
+Wine_histograms.png,"Considering the common semantics for Total phenols and Color intensity variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8572
+vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and PR AXISRECTANGULAR variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4928
+Wine_histograms.png,"Considering the common semantics for Total phenols and Proanthocyanins variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8562
+vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS and DISTANCE CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4826
+Wine_histograms.png,"Considering the common semantics for Total phenols and Alcalinity of ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8521
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst and radius_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11889
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and ELONGATEDNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4906
+heart_histograms.png,"Considering the common semantics for oldpeak and thalach variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3071
+Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst and perimeter_se variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11852
+adult_histograms.png,"Considering the common semantics for age and workclass variables, dummification if applied would increase the risk of facing the curse of dimensionality.",873
+vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MAX LENGTH ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4864
+Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se and perimeter_worst variables, dummification if applied would increase the risk of facing the curse of dimensionality.",11906
+vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5051
+Wine_histograms.png,"Considering the common semantics for Ash and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8580
+vehicle_histograms.png,"Considering the common semantics for CIRCULARITY and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4778
+heart_histograms.png,"Considering the common semantics for trestbps and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3042
+Churn_Modelling_histograms.png,"Considering the common semantics for Age and Balance variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2032
+vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO and MAJORKURTOSIS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",5054
+Wine_histograms.png,"Considering the common semantics for Alcohol and Ash variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8508
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO and CIRCULARITY variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4799
+vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and MINORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4966
+Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Hue variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8587
+vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4858
+adult_histograms.png,"Considering the common semantics for fnlwgt and age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",862
+adult_histograms.png,"Considering the common semantics for gender and capital-gain variables, dummification if applied would increase the risk of facing the curse of dimensionality.",989
+Iris_histograms.png,"Considering the common semantics for SepalLengthCm and PetalWidthCm variables, dummification if applied would increase the risk of facing the curse of dimensionality.",7828
+Churn_Modelling_histograms.png,"Considering the common semantics for Gender and EstimatedSalary variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2067
+heart_histograms.png,"Considering the common semantics for exang and sex variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2998
+Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines and Malic acid variables, dummification if applied would increase the risk of facing the curse of dimensionality.",8507
+vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS and MAJORVARIANCE variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4959
+diabetes_histograms.png,"Considering the common semantics for BMI and Insulin variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10546
+adult_histograms.png,"Considering the common semantics for hours-per-week and education variables, dummification if applied would increase the risk of facing the curse of dimensionality.",907
+Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and Gender variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2009
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4838
+heart_histograms.png,"Considering the common semantics for ca and restecg variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3061
+adult_histograms.png,"Considering the common semantics for marital-status and occupation variables, dummification if applied would increase the risk of facing the curse of dimensionality.",937
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE and RADIUS RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4837
+diabetes_histograms.png,"Considering the common semantics for BloodPressure and Age variables, dummification if applied would increase the risk of facing the curse of dimensionality.",10565
+Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary and Tenure variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2028
+heart_histograms.png,"Considering the common semantics for trestbps and slope variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3102
+vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY and COMPACTNESS variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4779
+vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR and PR AXIS ASPECT RATIO variables, dummification if applied would increase the risk of facing the curse of dimensionality.",4853
+heart_histograms.png,"Considering the common semantics for slope and fbs variables, dummification if applied would increase the risk of facing the curse of dimensionality.",3048
+Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember and HasCrCard variables, dummification if applied would increase the risk of facing the curse of dimensionality.",2054
+Wine_histograms.png,"Considering the common semantics for Malic acid variable, dummification would be the most adequate encoding.",8478
+adult_histograms.png,"Considering the common semantics for race variable, dummification would be the most adequate encoding.",856
+diabetes_histograms.png,"Considering the common semantics for Insulin variable, dummification would be the most adequate encoding.",10510
+vehicle_histograms.png,"Considering the common semantics for MAX LENGTH ASPECT RATIO variable, dummification would be the most adequate encoding.",4765
+Wine_histograms.png,"Considering the common semantics for Flavanoids variable, dummification would be the most adequate encoding.",8482
+vehicle_histograms.png,"Considering the common semantics for DISTANCE CIRCULARITY variable, dummification would be the most adequate encoding.",4762
+Iris_histograms.png,"Considering the common semantics for PetalLengthCm variable, dummification would be the most adequate encoding.",7817
+Breast_Cancer_histograms.png,"Considering the common semantics for smoothness_se variable, dummification would be the most adequate encoding.",11814
+Wine_histograms.png,"Considering the common semantics for Color intensity variable, dummification would be the most adequate encoding.",8485
+Churn_Modelling_histograms.png,"Considering the common semantics for EstimatedSalary variable, dummification would be the most adequate encoding.",1983
+Churn_Modelling_histograms.png,"Considering the common semantics for Age variable, dummification would be the most adequate encoding.",1977
+heart_histograms.png,"Considering the common semantics for oldpeak variable, dummification would be the most adequate encoding.",2975
+Wine_histograms.png,"Considering the common semantics for Ash variable, dummification would be the most adequate encoding.",8479
+adult_histograms.png,"Considering the common semantics for marital-status variable, dummification would be the most adequate encoding.",853
+Churn_Modelling_histograms.png,"Considering the common semantics for CreditScore variable, dummification would be the most adequate encoding.",1974
+Iris_histograms.png,"Considering the common semantics for PetalWidthCm variable, dummification would be the most adequate encoding.",7818
+Churn_Modelling_histograms.png,"Considering the common semantics for Tenure variable, dummification would be the most adequate encoding.",1978
+vehicle_histograms.png,"Considering the common semantics for COMPACTNESS variable, dummification would be the most adequate encoding.",4760
+Wine_histograms.png,"Considering the common semantics for Alcohol variable, dummification would be the most adequate encoding.",8477
+heart_histograms.png,"Considering the common semantics for fbs variable, dummification would be the most adequate encoding.",2971
+Churn_Modelling_histograms.png,"Considering the common semantics for HasCrCard variable, dummification would be the most adequate encoding.",1981
+vehicle_histograms.png,"Considering the common semantics for GYRATIONRADIUS variable, dummification would be the most adequate encoding.",4772
+Churn_Modelling_histograms.png,"Considering the common semantics for IsActiveMember variable, dummification would be the most adequate encoding.",1982
+heart_histograms.png,"Considering the common semantics for ca variable, dummification would be the most adequate encoding.",2977
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_se variable, dummification would be the most adequate encoding.",11812
+Churn_Modelling_histograms.png,"Considering the common semantics for Geography variable, dummification would be the most adequate encoding.",1975
+heart_histograms.png,"Considering the common semantics for age variable, dummification would be the most adequate encoding.",2966
+vehicle_histograms.png,"Considering the common semantics for CIRCULARITY variable, dummification would be the most adequate encoding.",4761
+Iris_histograms.png,"Considering the common semantics for SepalWidthCm variable, dummification would be the most adequate encoding.",7816
+Churn_Modelling_histograms.png,"Considering the common semantics for Gender variable, dummification would be the most adequate encoding.",1976
+vehicle_histograms.png,"Considering the common semantics for MAJORKURTOSIS variable, dummification would be the most adequate encoding.",4776
+heart_histograms.png,"Considering the common semantics for restecg variable, dummification would be the most adequate encoding.",2972
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_mean variable, dummification would be the most adequate encoding.",11810
+adult_histograms.png,"Considering the common semantics for relationship variable, dummification would be the most adequate encoding.",855
+adult_histograms.png,"Considering the common semantics for occupation variable, dummification would be the most adequate encoding.",854
+adult_histograms.png,"Considering the common semantics for gender variable, dummification would be the most adequate encoding.",857
+adult_histograms.png,"Considering the common semantics for capital-gain variable, dummification would be the most adequate encoding.",858
+Wine_histograms.png,"Considering the common semantics for Total phenols variable, dummification would be the most adequate encoding.",8481
+BankNoteAuthentication_histograms.png,"Considering the common semantics for skewness variable, dummification would be the most adequate encoding.",9945
+vehicle_histograms.png,"Considering the common semantics for LENGTHRECTANGULAR variable, dummification would be the most adequate encoding.",4769
+Breast_Cancer_histograms.png,"Considering the common semantics for perimeter_worst variable, dummification would be the most adequate encoding.",11818
+heart_histograms.png,"Considering the common semantics for exang variable, dummification would be the most adequate encoding.",2974
+Wine_histograms.png,"Considering the common semantics for Alcalinity of ash variable, dummification would be the most adequate encoding.",8480
+heart_histograms.png,"Considering the common semantics for thalach variable, dummification would be the most adequate encoding.",2973
+vehicle_histograms.png,"Considering the common semantics for MAJORVARIANCE variable, dummification would be the most adequate encoding.",4770
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_se variable, dummification would be the most adequate encoding.",11811
+diabetes_histograms.png,"Considering the common semantics for BloodPressure variable, dummification would be the most adequate encoding.",10508
+Breast_Cancer_histograms.png,"Considering the common semantics for radius_worst variable, dummification would be the most adequate encoding.",11816
+vehicle_histograms.png,"Considering the common semantics for MINORVARIANCE variable, dummification would be the most adequate encoding.",4771
+vehicle_histograms.png,"Considering the common semantics for MINORKURTOSIS variable, dummification would be the most adequate encoding.",4775
+Breast_Cancer_histograms.png,"Considering the common semantics for symmetry_se variable, dummification would be the most adequate encoding.",11815
+diabetes_histograms.png,"Considering the common semantics for Pregnancies variable, dummification would be the most adequate encoding.",10506
+BankNoteAuthentication_histograms.png,"Considering the common semantics for entropy variable, dummification would be the most adequate encoding.",9947
+diabetes_histograms.png,"Considering the common semantics for BMI variable, dummification would be the most adequate encoding.",10511
+BankNoteAuthentication_histograms.png,"Considering the common semantics for curtosis variable, dummification would be the most adequate encoding.",9946
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_worst variable, dummification would be the most adequate encoding.",11817
+heart_histograms.png,"Considering the common semantics for chol variable, dummification would be the most adequate encoding.",2970
+Wine_histograms.png,"Considering the common semantics for Hue variable, dummification would be the most adequate encoding.",8486
+Churn_Modelling_histograms.png,"Considering the common semantics for NumOfProducts variable, dummification would be the most adequate encoding.",1980
+vehicle_histograms.png,"Considering the common semantics for PR AXISRECTANGULAR variable, dummification would be the most adequate encoding.",4768
+Breast_Cancer_histograms.png,"Considering the common semantics for area_se variable, dummification would be the most adequate encoding.",11813
+Wine_histograms.png,"Considering the common semantics for Nonflavanoid phenols variable, dummification would be the most adequate encoding.",8483
+diabetes_histograms.png,"Considering the common semantics for Glucose variable, dummification would be the most adequate encoding.",10507
+Churn_Modelling_histograms.png,"Considering the common semantics for Balance variable, dummification would be the most adequate encoding.",1979
+Wine_histograms.png,"Considering the common semantics for Proanthocyanins variable, dummification would be the most adequate encoding.",8484
+heart_histograms.png,"Considering the common semantics for slope variable, dummification would be the most adequate encoding.",2976
+heart_histograms.png,"Considering the common semantics for thal variable, dummification would be the most adequate encoding.",2978
+adult_histograms.png,"Considering the common semantics for age variable, dummification would be the most adequate encoding.",848
+adult_histograms.png,"Considering the common semantics for fnlwgt variable, dummification would be the most adequate encoding.",850
+adult_histograms.png,"Considering the common semantics for educational-num variable, dummification would be the most adequate encoding.",852
+Breast_Cancer_histograms.png,"Considering the common semantics for texture_mean variable, dummification would be the most adequate encoding.",11809
+adult_histograms.png,"Considering the common semantics for capital-loss variable, dummification would be the most adequate encoding.",859
+vehicle_histograms.png,"Considering the common semantics for PR AXIS ASPECT RATIO variable, dummification would be the most adequate encoding.",4764
+vehicle_histograms.png,"Considering the common semantics for RADIUS RATIO variable, dummification would be the most adequate encoding.",4763
+heart_histograms.png,"Considering the common semantics for sex variable, dummification would be the most adequate encoding.",2967
+heart_histograms.png,"Considering the common semantics for cp variable, dummification would be the most adequate encoding.",2968
+adult_histograms.png,"Considering the common semantics for education variable, dummification would be the most adequate encoding.",851
+Wine_histograms.png,"Considering the common semantics for OD280-OD315 of diluted wines variable, dummification would be the most adequate encoding.",8487
+vehicle_histograms.png,"Considering the common semantics for MAJORSKEWNESS variable, dummification would be the most adequate encoding.",4773
+adult_histograms.png,"Considering the common semantics for hours-per-week variable, dummification would be the most adequate encoding.",860
+vehicle_histograms.png,"Considering the common semantics for SCATTER RATIO variable, dummification would be the most adequate encoding.",4766
+diabetes_histograms.png,"Considering the common semantics for Age variable, dummification would be the most adequate encoding.",10513
+diabetes_histograms.png,"Considering the common semantics for SkinThickness variable, dummification would be the most adequate encoding.",10509
+heart_histograms.png,"Considering the common semantics for trestbps variable, dummification would be the most adequate encoding.",2969
+vehicle_histograms.png,"Considering the common semantics for HOLLOWS RATIO variable, dummification would be the most adequate encoding.",4777
+adult_histograms.png,"Considering the common semantics for workclass variable, dummification would be the most adequate encoding.",849
+vehicle_histograms.png,"Considering the common semantics for ELONGATEDNESS variable, dummification would be the most adequate encoding.",4767
+vehicle_histograms.png,"Considering the common semantics for MINORSKEWNESS variable, dummification would be the most adequate encoding.",4774
+Iris_histograms.png,"Considering the common semantics for SepalLengthCm variable, dummification would be the most adequate encoding.",7815
+BankNoteAuthentication_histograms.png,"Considering the common semantics for variance variable, dummification would be the most adequate encoding.",9944
+diabetes_histograms.png,"Considering the common semantics for DiabetesPedigreeFunction variable, dummification would be the most adequate encoding.",10512
+Wine_histograms.png,The variable Flavanoids can be coded as ordinal without losing information.,8471
+heart_histograms.png,The variable thalach can be coded as ordinal without losing information.,2960
+heart_histograms.png,The variable age can be coded as ordinal without losing information.,2953
+diabetes_histograms.png,The variable Pregnancies can be coded as ordinal without losing information.,10498
+Breast_Cancer_histograms.png,The variable perimeter_se can be coded as ordinal without losing information.,11802
+diabetes_histograms.png,The variable Glucose can be coded as ordinal without losing information.,10499
+vehicle_histograms.png,The variable COMPACTNESS can be coded as ordinal without losing information.,4742
+Breast_Cancer_histograms.png,The variable texture_worst can be coded as ordinal without losing information.,11807
+diabetes_histograms.png,The variable Insulin can be coded as ordinal without losing information.,10502
+Breast_Cancer_histograms.png,The variable smoothness_se can be coded as ordinal without losing information.,11804
+adult_histograms.png,The variable fnlwgt can be coded as ordinal without losing information.,837
+Churn_Modelling_histograms.png,The variable Tenure can be coded as ordinal without losing information.,1968
+heart_histograms.png,The variable exang can be coded as ordinal without losing information.,2961
+adult_histograms.png,The variable occupation can be coded as ordinal without losing information.,841
+BankNoteAuthentication_histograms.png,The variable skewness can be coded as ordinal without losing information.,9941
+vehicle_histograms.png,The variable MINORSKEWNESS can be coded as ordinal without losing information.,4756
+vehicle_histograms.png,The variable MAJORSKEWNESS can be coded as ordinal without losing information.,4755
+vehicle_histograms.png,The variable MAJORKURTOSIS can be coded as ordinal without losing information.,4758
+Wine_histograms.png,The variable Hue can be coded as ordinal without losing information.,8475
+adult_histograms.png,The variable race can be coded as ordinal without losing information.,843
+Iris_histograms.png,The variable SepalWidthCm can be coded as ordinal without losing information.,7812
+vehicle_histograms.png,The variable MINORKURTOSIS can be coded as ordinal without losing information.,4757
+Wine_histograms.png,The variable Alcalinity of ash can be coded as ordinal without losing information.,8469
+adult_histograms.png,The variable capital-gain can be coded as ordinal without losing information.,845
+vehicle_histograms.png,The variable ELONGATEDNESS can be coded as ordinal without losing information.,4749
+adult_histograms.png,The variable workclass can be coded as ordinal without losing information.,836
+Wine_histograms.png,The variable Proanthocyanins can be coded as ordinal without losing information.,8473
+vehicle_histograms.png,The variable CIRCULARITY can be coded as ordinal without losing information.,4743
+Wine_histograms.png,The variable Ash can be coded as ordinal without losing information.,8468
+Wine_histograms.png,The variable OD280-OD315 of diluted wines can be coded as ordinal without losing information.,8476
+heart_histograms.png,The variable restecg can be coded as ordinal without losing information.,2959
+diabetes_histograms.png,The variable BMI can be coded as ordinal without losing information.,10503
+vehicle_histograms.png,The variable MAX LENGTH ASPECT RATIO can be coded as ordinal without losing information.,4747
+BankNoteAuthentication_histograms.png,The variable variance can be coded as ordinal without losing information.,9940
+vehicle_histograms.png,The variable MINORVARIANCE can be coded as ordinal without losing information.,4753
+adult_histograms.png,The variable education can be coded as ordinal without losing information.,838
+adult_histograms.png,The variable hours-per-week can be coded as ordinal without losing information.,847
+Churn_Modelling_histograms.png,The variable Age can be coded as ordinal without losing information.,1967
+diabetes_histograms.png,The variable DiabetesPedigreeFunction can be coded as ordinal without losing information.,10504
+adult_histograms.png,The variable age can be coded as ordinal without losing information.,835
+vehicle_histograms.png,The variable LENGTHRECTANGULAR can be coded as ordinal without losing information.,4751
+Churn_Modelling_histograms.png,The variable Gender can be coded as ordinal without losing information.,1966
+Breast_Cancer_histograms.png,The variable area_se can be coded as ordinal without losing information.,11803
+vehicle_histograms.png,The variable PR AXIS ASPECT RATIO can be coded as ordinal without losing information.,4746
+BankNoteAuthentication_histograms.png,The variable curtosis can be coded as ordinal without losing information.,9942
+Churn_Modelling_histograms.png,The variable Balance can be coded as ordinal without losing information.,1969
+Churn_Modelling_histograms.png,The variable CreditScore can be coded as ordinal without losing information.,1964
+Breast_Cancer_histograms.png,The variable texture_se can be coded as ordinal without losing information.,11801
+heart_histograms.png,The variable thal can be coded as ordinal without losing information.,2965
+vehicle_histograms.png,The variable HOLLOWS RATIO can be coded as ordinal without losing information.,4759
+Breast_Cancer_histograms.png,The variable perimeter_worst can be coded as ordinal without losing information.,11808
+Breast_Cancer_histograms.png,The variable perimeter_mean can be coded as ordinal without losing information.,11800
+heart_histograms.png,The variable sex can be coded as ordinal without losing information.,2954
+diabetes_histograms.png,The variable BloodPressure can be coded as ordinal without losing information.,10500
+vehicle_histograms.png,The variable MAJORVARIANCE can be coded as ordinal without losing information.,4752
+adult_histograms.png,The variable educational-num can be coded as ordinal without losing information.,839
+Breast_Cancer_histograms.png,The variable symmetry_se can be coded as ordinal without losing information.,11805
+heart_histograms.png,The variable slope can be coded as ordinal without losing information.,2963
+Churn_Modelling_histograms.png,The variable IsActiveMember can be coded as ordinal without losing information.,1972
+Wine_histograms.png,The variable Alcohol can be coded as ordinal without losing information.,8466
+diabetes_histograms.png,The variable Age can be coded as ordinal without losing information.,10505
+BankNoteAuthentication_histograms.png,The variable entropy can be coded as ordinal without losing information.,9943
+Iris_histograms.png,The variable PetalWidthCm can be coded as ordinal without losing information.,7814
+Wine_histograms.png,The variable Total phenols can be coded as ordinal without losing information.,8470
+Breast_Cancer_histograms.png,The variable texture_mean can be coded as ordinal without losing information.,11799
+vehicle_histograms.png,The variable DISTANCE CIRCULARITY can be coded as ordinal without losing information.,4744
+heart_histograms.png,The variable fbs can be coded as ordinal without losing information.,2958
+Churn_Modelling_histograms.png,The variable EstimatedSalary can be coded as ordinal without losing information.,1973
+heart_histograms.png,The variable ca can be coded as ordinal without losing information.,2964
+Wine_histograms.png,The variable Color intensity can be coded as ordinal without losing information.,8474
+adult_histograms.png,The variable marital-status can be coded as ordinal without losing information.,840
+Wine_histograms.png,The variable Malic acid can be coded as ordinal without losing information.,8467
+adult_histograms.png,The variable relationship can be coded as ordinal without losing information.,842
+Wine_histograms.png,The variable Nonflavanoid phenols can be coded as ordinal without losing information.,8472
+Iris_histograms.png,The variable PetalLengthCm can be coded as ordinal without losing information.,7813
+Iris_histograms.png,The variable SepalLengthCm can be coded as ordinal without losing information.,7811
+adult_histograms.png,The variable gender can be coded as ordinal without losing information.,844
+heart_histograms.png,The variable cp can be coded as ordinal without losing information.,2955
+vehicle_histograms.png,The variable PR AXISRECTANGULAR can be coded as ordinal without losing information.,4750
+Churn_Modelling_histograms.png,The variable HasCrCard can be coded as ordinal without losing information.,1971
+vehicle_histograms.png,The variable GYRATIONRADIUS can be coded as ordinal without losing information.,4754
+Churn_Modelling_histograms.png,The variable Geography can be coded as ordinal without losing information.,1965
+diabetes_histograms.png,The variable SkinThickness can be coded as ordinal without losing information.,10501
+heart_histograms.png,The variable chol can be coded as ordinal without losing information.,2957
+heart_histograms.png,The variable oldpeak can be coded as ordinal without losing information.,2962
+vehicle_histograms.png,The variable RADIUS RATIO can be coded as ordinal without losing information.,4745
+heart_histograms.png,The variable trestbps can be coded as ordinal without losing information.,2956
+adult_histograms.png,The variable capital-loss can be coded as ordinal without losing information.,846
+Churn_Modelling_histograms.png,The variable NumOfProducts can be coded as ordinal without losing information.,1970
+vehicle_histograms.png,The variable SCATTER RATIO can be coded as ordinal without losing information.,4748
+Breast_Cancer_histograms.png,The variable radius_worst can be coded as ordinal without losing information.,11806
+adult_mv.png,Discarding variables capital-gain and education would be better than discarding all the records with missing values for those variables.,724
+adult_mv.png,Discarding variables race and gender would be better than discarding all the records with missing values for those variables.,795
+adult_mv.png,Discarding variables hours-per-week and capital-loss would be better than discarding all the records with missing values for those variables.,822
+adult_mv.png,Discarding variables age and gender would be better than discarding all the records with missing values for those variables.,787
+adult_mv.png,Discarding variables hours-per-week and gender would be better than discarding all the records with missing values for those variables.,798
+adult_mv.png,Discarding variables occupation and educational-num would be better than discarding all the records with missing values for those variables.,732
+adult_mv.png,Discarding variables education and race would be better than discarding all the records with missing values for those variables.,778
+adult_mv.png,Discarding variables capital-gain and marital-status would be better than discarding all the records with missing values for those variables.,748
+adult_mv.png,Discarding variables age and occupation would be better than discarding all the records with missing values for those variables.,751
+adult_mv.png,Discarding variables age and marital-status would be better than discarding all the records with missing values for those variables.,739
+adult_mv.png,Discarding variables hours-per-week and education would be better than discarding all the records with missing values for those variables.,726
+adult_mv.png,Discarding variables gender and occupation would be better than discarding all the records with missing values for those variables.,759
+adult_mv.png,Discarding variables marital-status and age would be better than discarding all the records with missing values for those variables.,684
+adult_mv.png,Discarding variables age and education would be better than discarding all the records with missing values for those variables.,716
+adult_mv.png,Discarding variables hours-per-week and educational-num would be better than discarding all the records with missing values for those variables.,738
+adult_mv.png,Discarding variables marital-status and race would be better than discarding all the records with missing values for those variables.,780
+adult_mv.png,Discarding variables fnlwgt and workclass would be better than discarding all the records with missing values for those variables.,693
+adult_mv.png,Discarding variables occupation and race would be better than discarding all the records with missing values for those variables.,781
+adult_mv.png,Discarding variables fnlwgt and hours-per-week would be better than discarding all the records with missing values for those variables.,825
+adult_mv.png,Discarding variables race and fnlwgt would be better than discarding all the records with missing values for those variables.,711
+adult_mv.png,Discarding variables capital-loss and educational-num would be better than discarding all the records with missing values for those variables.,737
+adult_mv.png,Discarding variables workclass and education would be better than discarding all the records with missing values for those variables.,717
+adult_mv.png,Discarding variables capital-loss and race would be better than discarding all the records with missing values for those variables.,785
+adult_mv.png,Discarding variables race and educational-num would be better than discarding all the records with missing values for those variables.,734
+adult_mv.png,Discarding variables gender and education would be better than discarding all the records with missing values for those variables.,723
+adult_mv.png,Discarding variables capital-gain and hours-per-week would be better than discarding all the records with missing values for those variables.,833
+adult_mv.png,Discarding variables relationship and occupation would be better than discarding all the records with missing values for those variables.,757
+adult_mv.png,Discarding variables capital-loss and age would be better than discarding all the records with missing values for those variables.,690
+adult_mv.png,Discarding variables hours-per-week and marital-status would be better than discarding all the records with missing values for those variables.,750
+adult_mv.png,Discarding variables occupation and education would be better than discarding all the records with missing values for those variables.,720
+adult_mv.png,Discarding variables marital-status and capital-loss would be better than discarding all the records with missing values for those variables.,816
+adult_mv.png,Discarding variables educational-num and capital-gain would be better than discarding all the records with missing values for those variables.,803
+adult_mv.png,Discarding variables occupation and relationship would be better than discarding all the records with missing values for those variables.,769
+adult_mv.png,Discarding variables educational-num and hours-per-week would be better than discarding all the records with missing values for those variables.,827
+adult_mv.png,Discarding variables capital-loss and workclass would be better than discarding all the records with missing values for those variables.,702
+adult_mv.png,Discarding variables relationship and fnlwgt would be better than discarding all the records with missing values for those variables.,710
+adult_mv.png,Discarding variables race and relationship would be better than discarding all the records with missing values for those variables.,770
+adult_mv.png,Discarding variables relationship and education would be better than discarding all the records with missing values for those variables.,721
+adult_mv.png,Discarding variables race and capital-loss would be better than discarding all the records with missing values for those variables.,819
+adult_mv.png,Discarding variables capital-loss and hours-per-week would be better than discarding all the records with missing values for those variables.,834
+adult_mv.png,Discarding variables capital-gain and race would be better than discarding all the records with missing values for those variables.,784
+adult_mv.png,Discarding variables marital-status and gender would be better than discarding all the records with missing values for those variables.,792
+adult_mv.png,Discarding variables fnlwgt and marital-status would be better than discarding all the records with missing values for those variables.,741
+adult_mv.png,Discarding variables workclass and educational-num would be better than discarding all the records with missing values for those variables.,728
+adult_mv.png,Discarding variables education and capital-loss would be better than discarding all the records with missing values for those variables.,814
+adult_mv.png,Discarding variables workclass and capital-loss would be better than discarding all the records with missing values for those variables.,812
+adult_mv.png,Discarding variables hours-per-week and workclass would be better than discarding all the records with missing values for those variables.,703
+adult_mv.png,Discarding variables capital-gain and educational-num would be better than discarding all the records with missing values for those variables.,736
+adult_mv.png,Discarding variables age and hours-per-week would be better than discarding all the records with missing values for those variables.,823
+adult_mv.png,Discarding variables age and race would be better than discarding all the records with missing values for those variables.,775
+adult_mv.png,Discarding variables workclass and hours-per-week would be better than discarding all the records with missing values for those variables.,824
+adult_mv.png,Discarding variables capital-loss and capital-gain would be better than discarding all the records with missing values for those variables.,809
+adult_mv.png,Discarding variables hours-per-week and age would be better than discarding all the records with missing values for those variables.,691
+adult_mv.png,Discarding variables gender and capital-loss would be better than discarding all the records with missing values for those variables.,820
+adult_mv.png,Discarding variables marital-status and capital-gain would be better than discarding all the records with missing values for those variables.,804
+adult_mv.png,Discarding variables race and age would be better than discarding all the records with missing values for those variables.,687
+adult_mv.png,Discarding variables age and fnlwgt would be better than discarding all the records with missing values for those variables.,704
+adult_mv.png,Discarding variables capital-loss and gender would be better than discarding all the records with missing values for those variables.,797
+adult_mv.png,Discarding variables relationship and workclass would be better than discarding all the records with missing values for those variables.,698
+adult_mv.png,Discarding variables capital-loss and occupation would be better than discarding all the records with missing values for those variables.,761
+adult_mv.png,Discarding variables educational-num and race would be better than discarding all the records with missing values for those variables.,779
+adult_mv.png,Discarding variables hours-per-week and relationship would be better than discarding all the records with missing values for those variables.,774
+adult_mv.png,Discarding variables relationship and gender would be better than discarding all the records with missing values for those variables.,794
+adult_mv.png,Discarding variables age and capital-gain would be better than discarding all the records with missing values for those variables.,799
+adult_mv.png,Discarding variables occupation and capital-loss would be better than discarding all the records with missing values for those variables.,817
+adult_mv.png,Discarding variables capital-loss and fnlwgt would be better than discarding all the records with missing values for those variables.,714
+adult_mv.png,Discarding variables gender and fnlwgt would be better than discarding all the records with missing values for those variables.,712
+adult_mv.png,Discarding variables fnlwgt and capital-loss would be better than discarding all the records with missing values for those variables.,813
+adult_mv.png,Discarding variables education and relationship would be better than discarding all the records with missing values for those variables.,766
+adult_mv.png,Discarding variables workclass and capital-gain would be better than discarding all the records with missing values for those variables.,800
+adult_mv.png,Discarding variables marital-status and workclass would be better than discarding all the records with missing values for those variables.,696
+adult_mv.png,Discarding variables educational-num and workclass would be better than discarding all the records with missing values for those variables.,695
+adult_mv.png,Discarding variables age and capital-loss would be better than discarding all the records with missing values for those variables.,811
+adult_mv.png,Discarding variables fnlwgt and age would be better than discarding all the records with missing values for those variables.,681
+adult_mv.png,Discarding variables education and occupation would be better than discarding all the records with missing values for those variables.,754
+adult_mv.png,Discarding variables relationship and capital-loss would be better than discarding all the records with missing values for those variables.,818
+adult_mv.png,Discarding variables capital-gain and capital-loss would be better than discarding all the records with missing values for those variables.,821
+adult_mv.png,Discarding variables relationship and educational-num would be better than discarding all the records with missing values for those variables.,733
+adult_mv.png,Discarding variables fnlwgt and gender would be better than discarding all the records with missing values for those variables.,789
+adult_mv.png,Discarding variables age and relationship would be better than discarding all the records with missing values for those variables.,763
+adult_mv.png,Discarding variables education and gender would be better than discarding all the records with missing values for those variables.,790
+adult_mv.png,Discarding variables age and workclass would be better than discarding all the records with missing values for those variables.,692
+adult_mv.png,Discarding variables occupation and fnlwgt would be better than discarding all the records with missing values for those variables.,709
+adult_mv.png,Discarding variables gender and workclass would be better than discarding all the records with missing values for those variables.,700
+adult_mv.png,Discarding variables education and hours-per-week would be better than discarding all the records with missing values for those variables.,826
+adult_mv.png,Discarding variables fnlwgt and education would be better than discarding all the records with missing values for those variables.,718
+adult_mv.png,Discarding variables hours-per-week and occupation would be better than discarding all the records with missing values for those variables.,762
+adult_mv.png,Discarding variables capital-gain and age would be better than discarding all the records with missing values for those variables.,689
+adult_mv.png,Discarding variables gender and hours-per-week would be better than discarding all the records with missing values for those variables.,832
+adult_mv.png,Discarding variables occupation and gender would be better than discarding all the records with missing values for those variables.,793
+adult_mv.png,Discarding variables marital-status and education would be better than discarding all the records with missing values for those variables.,719
+adult_mv.png,Discarding variables capital-gain and fnlwgt would be better than discarding all the records with missing values for those variables.,713
+adult_mv.png,Discarding variables age and educational-num would be better than discarding all the records with missing values for those variables.,727
+adult_mv.png,Discarding variables education and fnlwgt would be better than discarding all the records with missing values for those variables.,706
+adult_mv.png,Discarding variables occupation and capital-gain would be better than discarding all the records with missing values for those variables.,805
+adult_mv.png,Discarding variables capital-gain and occupation would be better than discarding all the records with missing values for those variables.,760
+adult_mv.png,Discarding variables capital-gain and gender would be better than discarding all the records with missing values for those variables.,796
+adult_mv.png,Discarding variables race and marital-status would be better than discarding all the records with missing values for those variables.,746
+adult_mv.png,Discarding variables education and educational-num would be better than discarding all the records with missing values for those variables.,730
+adult_mv.png,Discarding variables hours-per-week and fnlwgt would be better than discarding all the records with missing values for those variables.,715
+adult_mv.png,Discarding variables marital-status and occupation would be better than discarding all the records with missing values for those variables.,756
+adult_mv.png,Discarding variables marital-status and educational-num would be better than discarding all the records with missing values for those variables.,731
+adult_mv.png,Discarding variables fnlwgt and race would be better than discarding all the records with missing values for those variables.,777
+adult_mv.png,Discarding variables fnlwgt and educational-num would be better than discarding all the records with missing values for those variables.,729
+adult_mv.png,Discarding variables occupation and age would be better than discarding all the records with missing values for those variables.,685
+adult_mv.png,Discarding variables workclass and gender would be better than discarding all the records with missing values for those variables.,788
+adult_mv.png,Discarding variables hours-per-week and race would be better than discarding all the records with missing values for those variables.,786
+adult_mv.png,Discarding variables education and workclass would be better than discarding all the records with missing values for those variables.,694
+adult_mv.png,Discarding variables fnlwgt and occupation would be better than discarding all the records with missing values for those variables.,753
+adult_mv.png,Discarding variables educational-num and gender would be better than discarding all the records with missing values for those variables.,791
+adult_mv.png,Discarding variables educational-num and relationship would be better than discarding all the records with missing values for those variables.,767
+adult_mv.png,Discarding variables marital-status and fnlwgt would be better than discarding all the records with missing values for those variables.,708
+adult_mv.png,Discarding variables relationship and race would be better than discarding all the records with missing values for those variables.,782
+adult_mv.png,Discarding variables relationship and marital-status would be better than discarding all the records with missing values for those variables.,745
+adult_mv.png,Discarding variables capital-loss and relationship would be better than discarding all the records with missing values for those variables.,773
+adult_mv.png,Discarding variables fnlwgt and capital-gain would be better than discarding all the records with missing values for those variables.,801
+adult_mv.png,Discarding variables relationship and age would be better than discarding all the records with missing values for those variables.,686
+adult_mv.png,Discarding variables race and workclass would be better than discarding all the records with missing values for those variables.,699
+adult_mv.png,Discarding variables education and age would be better than discarding all the records with missing values for those variables.,682
+adult_mv.png,Discarding variables workclass and occupation would be better than discarding all the records with missing values for those variables.,752
+adult_mv.png,Discarding variables gender and capital-gain would be better than discarding all the records with missing values for those variables.,808
+adult_mv.png,Discarding variables workclass and fnlwgt would be better than discarding all the records with missing values for those variables.,705
+adult_mv.png,Discarding variables occupation and marital-status would be better than discarding all the records with missing values for those variables.,744
+adult_mv.png,Discarding variables race and hours-per-week would be better than discarding all the records with missing values for those variables.,831
+adult_mv.png,Discarding variables fnlwgt and relationship would be better than discarding all the records with missing values for those variables.,765
+adult_mv.png,Discarding variables capital-loss and education would be better than discarding all the records with missing values for those variables.,725
+adult_mv.png,Discarding variables occupation and hours-per-week would be better than discarding all the records with missing values for those variables.,829
+adult_mv.png,Discarding variables capital-gain and relationship would be better than discarding all the records with missing values for those variables.,772
+adult_mv.png,Discarding variables educational-num and fnlwgt would be better than discarding all the records with missing values for those variables.,707
+adult_mv.png,Discarding variables gender and age would be better than discarding all the records with missing values for those variables.,688
+adult_mv.png,Discarding variables capital-gain and workclass would be better than discarding all the records with missing values for those variables.,701
+adult_mv.png,Discarding variables educational-num and age would be better than discarding all the records with missing values for those variables.,683
+adult_mv.png,Discarding variables relationship and capital-gain would be better than discarding all the records with missing values for those variables.,806
+adult_mv.png,Discarding variables workclass and race would be better than discarding all the records with missing values for those variables.,776
+adult_mv.png,Discarding variables gender and race would be better than discarding all the records with missing values for those variables.,783
+adult_mv.png,Discarding variables education and marital-status would be better than discarding all the records with missing values for those variables.,742
+adult_mv.png,Discarding variables hours-per-week and capital-gain would be better than discarding all the records with missing values for those variables.,810
+adult_mv.png,Discarding variables workclass and age would be better than discarding all the records with missing values for those variables.,680
+adult_mv.png,Discarding variables educational-num and capital-loss would be better than discarding all the records with missing values for those variables.,815
+adult_mv.png,Discarding variables gender and educational-num would be better than discarding all the records with missing values for those variables.,735
+adult_mv.png,Discarding variables education and capital-gain would be better than discarding all the records with missing values for those variables.,802
+adult_mv.png,Discarding variables workclass and marital-status would be better than discarding all the records with missing values for those variables.,740
+adult_mv.png,Discarding variables marital-status and hours-per-week would be better than discarding all the records with missing values for those variables.,828
+adult_mv.png,Discarding variables capital-loss and marital-status would be better than discarding all the records with missing values for those variables.,749
+adult_mv.png,Discarding variables workclass and relationship would be better than discarding all the records with missing values for those variables.,764
+adult_mv.png,Discarding variables gender and marital-status would be better than discarding all the records with missing values for those variables.,747
+adult_mv.png,Discarding variables educational-num and marital-status would be better than discarding all the records with missing values for those variables.,743
+adult_mv.png,Discarding variables race and occupation would be better than discarding all the records with missing values for those variables.,758
+adult_mv.png,Discarding variables marital-status and relationship would be better than discarding all the records with missing values for those variables.,768
+adult_mv.png,Discarding variables educational-num and occupation would be better than discarding all the records with missing values for those variables.,755
+adult_mv.png,Discarding variables race and education would be better than discarding all the records with missing values for those variables.,722
+adult_mv.png,Discarding variables race and capital-gain would be better than discarding all the records with missing values for those variables.,807
+adult_mv.png,Discarding variables gender and relationship would be better than discarding all the records with missing values for those variables.,771
+adult_mv.png,Discarding variables relationship and hours-per-week would be better than discarding all the records with missing values for those variables.,830
+adult_mv.png,Discarding variables occupation and workclass would be better than discarding all the records with missing values for those variables.,697
+adult_mv.png,Dropping all rows with missing values can lead to a dataset with less than 30% of the original data.,677
+adult_mv.png,Dropping all rows with missing values can lead to a dataset with less than 25% of the original data.,676
+adult_mv.png,Dropping all rows with missing values can lead to a dataset with less than 40% of the original data.,678
+adult_histograms.png,Feature generation based on both variables workclass and gender seems to be promising.,628
+adult_histograms.png,Feature generation based on both variables race and age seems to be promising.,527
+adult_histograms.png,Feature generation based on both variables capital-loss and workclass seems to be promising.,542
+adult_histograms.png,Feature generation based on both variables workclass and occupation seems to be promising.,592
+adult_histograms.png,Feature generation based on both variables capital-gain and hours-per-week seems to be promising.,673
+adult_histograms.png,Feature generation based on both variables occupation and education seems to be promising.,560
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and perimeter_mean seems to be promising.,11629
+adult_histograms.png,Feature generation based on both variables capital-loss and educational-num seems to be promising.,577
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and perimeter_se seems to be promising.,11647
+adult_histograms.png,Feature generation based on both variables fnlwgt and occupation seems to be promising.,593
+adult_histograms.png,Feature generation based on both variables educational-num and fnlwgt seems to be promising.,547
+adult_histograms.png,Feature generation based on both variables educational-num and marital-status seems to be promising.,583
+adult_histograms.png,Feature generation based on both variables education and occupation seems to be promising.,594
+adult_histograms.png,Feature generation based on both variables hours-per-week and race seems to be promising.,626
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and texture_worst seems to be promising.,11687
+adult_histograms.png,Feature generation based on both variables gender and educational-num seems to be promising.,575
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and perimeter_mean seems to be promising.,11623
+adult_histograms.png,Feature generation based on both variables race and capital-gain seems to be promising.,647
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and texture_se seems to be promising.,11639
+adult_histograms.png,Feature generation based on both variables capital-gain and capital-loss seems to be promising.,661
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and radius_worst seems to be promising.,11680
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and smoothness_se seems to be promising.,11664
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and symmetry_se seems to be promising.,11670
+adult_histograms.png,Feature generation based on both variables capital-gain and education seems to be promising.,564
+adult_histograms.png,Feature generation based on both variables occupation and educational-num seems to be promising.,572
+adult_histograms.png,Feature generation based on both variables marital-status and race seems to be promising.,620
+adult_histograms.png,Feature generation based on both variables marital-status and age seems to be promising.,524
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and texture_mean seems to be promising.,11622
+adult_histograms.png,Feature generation based on both variables occupation and age seems to be promising.,525
+adult_histograms.png,Feature generation based on both variables hours-per-week and education seems to be promising.,566
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and perimeter_se seems to be promising.,11645
+adult_histograms.png,Feature generation based on both variables relationship and hours-per-week seems to be promising.,670
+adult_histograms.png,Feature generation based on both variables capital-loss and hours-per-week seems to be promising.,674
+adult_histograms.png,Feature generation based on both variables capital-gain and workclass seems to be promising.,541
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and perimeter_mean seems to be promising.,11628
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and symmetry_se seems to be promising.,11676
+adult_histograms.png,Feature generation based on both variables occupation and capital-loss seems to be promising.,657
+adult_histograms.png,Feature generation based on both variables capital-loss and marital-status seems to be promising.,589
+adult_histograms.png,Feature generation based on both variables occupation and relationship seems to be promising.,609
+adult_histograms.png,Feature generation based on both variables gender and relationship seems to be promising.,611
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and radius_worst seems to be promising.,11678
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and perimeter_worst seems to be promising.,11702
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and texture_se seems to be promising.,11640
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and texture_worst seems to be promising.,11693
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and perimeter_se seems to be promising.,11644
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and area_se seems to be promising.,11658
+adult_histograms.png,Feature generation based on both variables age and race seems to be promising.,615
+adult_histograms.png,Feature generation based on both variables educational-num and race seems to be promising.,619
+adult_histograms.png,Feature generation based on both variables educational-num and age seems to be promising.,523
+adult_histograms.png,Feature generation based on both variables fnlwgt and age seems to be promising.,521
+adult_histograms.png,Feature generation based on both variables fnlwgt and workclass seems to be promising.,533
+adult_histograms.png,Feature generation based on both variables gender and marital-status seems to be promising.,587
+adult_histograms.png,Feature generation based on both variables capital-loss and fnlwgt seems to be promising.,554
+adult_histograms.png,Feature generation based on both variables occupation and workclass seems to be promising.,537
+adult_histograms.png,Feature generation based on both variables fnlwgt and race seems to be promising.,617
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and radius_worst seems to be promising.,11682
+adult_histograms.png,Feature generation based on both variables hours-per-week and capital-loss seems to be promising.,662
+adult_histograms.png,Feature generation based on both variables education and race seems to be promising.,618
+adult_histograms.png,Feature generation based on both variables relationship and gender seems to be promising.,634
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and smoothness_se seems to be promising.,11666
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and perimeter_se seems to be promising.,11643
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and symmetry_se seems to be promising.,11675
+adult_histograms.png,Feature generation based on both variables marital-status and education seems to be promising.,559
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and smoothness_se seems to be promising.,11667
+adult_histograms.png,Feature generation based on both variables fnlwgt and gender seems to be promising.,629
+adult_histograms.png,Feature generation based on both variables capital-gain and gender seems to be promising.,636
+adult_histograms.png,Feature generation based on both variables gender and race seems to be promising.,623
+adult_histograms.png,Feature generation based on both variables gender and education seems to be promising.,563
+adult_histograms.png,Feature generation based on both variables fnlwgt and relationship seems to be promising.,605
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and texture_worst seems to be promising.,11694
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and texture_worst seems to be promising.,11686
+adult_histograms.png,Feature generation based on both variables relationship and race seems to be promising.,622
+adult_histograms.png,Feature generation based on both variables capital-loss and capital-gain seems to be promising.,649
+adult_histograms.png,Feature generation based on both variables race and marital-status seems to be promising.,586
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and perimeter_worst seems to be promising.,11695
+adult_histograms.png,Feature generation based on both variables gender and age seems to be promising.,528
+adult_histograms.png,Feature generation based on both variables fnlwgt and educational-num seems to be promising.,569
+adult_histograms.png,Feature generation based on both variables relationship and education seems to be promising.,561
+adult_histograms.png,Feature generation based on both variables capital-gain and relationship seems to be promising.,612
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and symmetry_se seems to be promising.,11669
+adult_histograms.png,Feature generation based on both variables education and hours-per-week seems to be promising.,666
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and texture_mean seems to be promising.,11616
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and symmetry_se seems to be promising.,11673
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and perimeter_mean seems to be promising.,11630
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and smoothness_se seems to be promising.,11662
+adult_histograms.png,Feature generation based on both variables occupation and race seems to be promising.,621
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and perimeter_se seems to be promising.,11649
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and perimeter_worst seems to be promising.,11703
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and perimeter_worst seems to be promising.,11698
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and perimeter_mean seems to be promising.,11626
+adult_histograms.png,Feature generation based on both variables marital-status and hours-per-week seems to be promising.,668
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and texture_worst seems to be promising.,11688
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and perimeter_mean seems to be promising.,11625
+adult_histograms.png,Feature generation based on both variables relationship and age seems to be promising.,526
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and texture_se seems to be promising.,11634
+adult_histograms.png,Feature generation based on both variables educational-num and capital-loss seems to be promising.,655
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and smoothness_se seems to be promising.,11661
+adult_histograms.png,Feature generation based on both variables workclass and education seems to be promising.,557
+adult_histograms.png,Feature generation based on both variables hours-per-week and capital-gain seems to be promising.,650
+adult_histograms.png,Feature generation based on both variables fnlwgt and marital-status seems to be promising.,581
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and texture_se seems to be promising.,11633
+adult_histograms.png,Feature generation based on both variables fnlwgt and capital-gain seems to be promising.,641
+adult_histograms.png,Feature generation based on both variables race and educational-num seems to be promising.,574
+adult_histograms.png,Feature generation based on both variables age and capital-loss seems to be promising.,651
+adult_histograms.png,Feature generation based on both variables capital-gain and marital-status seems to be promising.,588
+adult_histograms.png,Feature generation based on both variables marital-status and occupation seems to be promising.,596
+adult_histograms.png,Feature generation based on both variables hours-per-week and workclass seems to be promising.,543
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and radius_worst seems to be promising.,11684
+adult_histograms.png,Feature generation based on both variables occupation and capital-gain seems to be promising.,645
+adult_histograms.png,Feature generation based on both variables workclass and age seems to be promising.,520
+adult_histograms.png,Feature generation based on both variables marital-status and fnlwgt seems to be promising.,548
+adult_histograms.png,Feature generation based on both variables workclass and race seems to be promising.,616
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and texture_mean seems to be promising.,11621
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and radius_worst seems to be promising.,11685
+adult_histograms.png,Feature generation based on both variables occupation and gender seems to be promising.,633
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and perimeter_se seems to be promising.,11641
+adult_histograms.png,Feature generation based on both variables marital-status and educational-num seems to be promising.,571
+adult_histograms.png,Feature generation based on both variables age and marital-status seems to be promising.,579
+adult_histograms.png,Feature generation based on both variables workclass and relationship seems to be promising.,604
+adult_histograms.png,Feature generation based on both variables capital-gain and educational-num seems to be promising.,576
+adult_histograms.png,Feature generation based on both variables capital-loss and age seems to be promising.,530
+adult_histograms.png,Feature generation based on both variables race and occupation seems to be promising.,598
+adult_histograms.png,Feature generation based on both variables workclass and fnlwgt seems to be promising.,545
+adult_histograms.png,Feature generation based on both variables race and workclass seems to be promising.,539
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and texture_mean seems to be promising.,11617
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and texture_worst seems to be promising.,11690
+adult_histograms.png,Feature generation based on both variables education and relationship seems to be promising.,606
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and texture_mean seems to be promising.,11619
+adult_histograms.png,Feature generation based on both variables capital-loss and gender seems to be promising.,637
+adult_histograms.png,Feature generation based on both variables age and workclass seems to be promising.,532
+adult_histograms.png,Feature generation based on both variables education and gender seems to be promising.,630
+adult_histograms.png,Feature generation based on both variables marital-status and gender seems to be promising.,632
+adult_histograms.png,Feature generation based on both variables race and relationship seems to be promising.,610
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and area_se seems to be promising.,11654
+adult_histograms.png,Feature generation based on both variables capital-loss and relationship seems to be promising.,613
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and area_se seems to be promising.,11651
+adult_histograms.png,Feature generation based on both variables education and marital-status seems to be promising.,582
+adult_histograms.png,Feature generation based on both variables age and gender seems to be promising.,627
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and smoothness_se seems to be promising.,11659
+adult_histograms.png,Feature generation based on both variables age and fnlwgt seems to be promising.,544
+adult_histograms.png,Feature generation based on both variables hours-per-week and age seems to be promising.,531
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and texture_mean seems to be promising.,11614
+adult_histograms.png,Feature generation based on both variables education and fnlwgt seems to be promising.,546
+adult_histograms.png,Feature generation based on both variables fnlwgt and capital-loss seems to be promising.,653
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and texture_se seems to be promising.,11637
+adult_histograms.png,Feature generation based on both variables marital-status and capital-gain seems to be promising.,644
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and texture_worst seems to be promising.,11689
+adult_histograms.png,Feature generation based on both variables hours-per-week and occupation seems to be promising.,602
+adult_histograms.png,Feature generation based on both variables hours-per-week and educational-num seems to be promising.,578
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and perimeter_worst seems to be promising.,11699
+adult_histograms.png,Feature generation based on both variables age and occupation seems to be promising.,591
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and perimeter_se seems to be promising.,11642
+adult_histograms.png,Feature generation based on both variables education and capital-gain seems to be promising.,642
+adult_histograms.png,Feature generation based on both variables capital-gain and race seems to be promising.,624
+adult_histograms.png,Feature generation based on both variables educational-num and hours-per-week seems to be promising.,667
+adult_histograms.png,Feature generation based on both variables relationship and occupation seems to be promising.,597
+adult_histograms.png,Feature generation based on both variables education and workclass seems to be promising.,534
+adult_histograms.png,Feature generation based on both variables occupation and hours-per-week seems to be promising.,669
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and smoothness_se seems to be promising.,11665
+adult_histograms.png,Feature generation based on both variables gender and fnlwgt seems to be promising.,552
+adult_histograms.png,Feature generation based on both variables gender and capital-gain seems to be promising.,648
+adult_histograms.png,Feature generation based on both variables occupation and marital-status seems to be promising.,584
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and symmetry_se seems to be promising.,11668
+adult_histograms.png,Feature generation based on both variables gender and occupation seems to be promising.,599
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and symmetry_se seems to be promising.,11672
+adult_histograms.png,Feature generation based on both variables workclass and educational-num seems to be promising.,568
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and perimeter_mean seems to be promising.,11624
+adult_histograms.png,Feature generation based on both variables hours-per-week and gender seems to be promising.,638
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and area_se seems to be promising.,11656
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and texture_se seems to be promising.,11636
+adult_histograms.png,Feature generation based on both variables hours-per-week and relationship seems to be promising.,614
+adult_histograms.png,Feature generation based on both variables race and capital-loss seems to be promising.,659
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and area_se seems to be promising.,11650
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and texture_worst seems to be promising.,11692
+adult_histograms.png,Feature generation based on both variables education and age seems to be promising.,522
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and perimeter_worst seems to be promising.,11700
+adult_histograms.png,Feature generation based on both variables gender and capital-loss seems to be promising.,660
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and perimeter_mean seems to be promising.,11627
+adult_histograms.png,Feature generation based on both variables workclass and marital-status seems to be promising.,580
+adult_histograms.png,Feature generation based on both variables capital-loss and education seems to be promising.,565
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and perimeter_se seems to be promising.,11648
+adult_histograms.png,Feature generation based on both variables capital-gain and occupation seems to be promising.,600
+adult_histograms.png,Feature generation based on both variables hours-per-week and marital-status seems to be promising.,590
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and texture_se seems to be promising.,11635
+adult_histograms.png,Feature generation based on both variables education and capital-loss seems to be promising.,654
+adult_histograms.png,Feature generation based on both variables race and hours-per-week seems to be promising.,671
+adult_histograms.png,Feature generation based on both variables fnlwgt and hours-per-week seems to be promising.,665
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and perimeter_worst seems to be promising.,11696
+adult_histograms.png,Feature generation based on both variables educational-num and capital-gain seems to be promising.,643
+adult_histograms.png,Feature generation based on both variables occupation and fnlwgt seems to be promising.,549
+adult_histograms.png,Feature generation based on both variables marital-status and capital-loss seems to be promising.,656
+adult_histograms.png,Feature generation based on both variables capital-loss and occupation seems to be promising.,601
+adult_histograms.png,Feature generation based on both variables educational-num and workclass seems to be promising.,535
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and texture_mean seems to be promising.,11618
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and smoothness_se seems to be promising.,11663
+adult_histograms.png,Feature generation based on both variables capital-loss and race seems to be promising.,625
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and texture_se seems to be promising.,11632
+Breast_Cancer_histograms.png,Feature generation based on both variables area_se and radius_worst seems to be promising.,11681
+adult_histograms.png,Feature generation based on both variables educational-num and relationship seems to be promising.,607
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and symmetry_se seems to be promising.,11674
+adult_histograms.png,Feature generation based on both variables fnlwgt and education seems to be promising.,558
+adult_histograms.png,Feature generation based on both variables hours-per-week and fnlwgt seems to be promising.,555
+adult_histograms.png,Feature generation based on both variables age and capital-gain seems to be promising.,639
+adult_histograms.png,Feature generation based on both variables relationship and marital-status seems to be promising.,585
+adult_histograms.png,Feature generation based on both variables race and fnlwgt seems to be promising.,551
+adult_histograms.png,Feature generation based on both variables educational-num and occupation seems to be promising.,595
+adult_histograms.png,Feature generation based on both variables relationship and workclass seems to be promising.,538
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and texture_mean seems to be promising.,11620
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_worst and area_se seems to be promising.,11657
+adult_histograms.png,Feature generation based on both variables capital-gain and fnlwgt seems to be promising.,553
+adult_histograms.png,Feature generation based on both variables education and educational-num seems to be promising.,570
+adult_histograms.png,Feature generation based on both variables workclass and capital-gain seems to be promising.,640
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_worst and perimeter_mean seems to be promising.,11631
+adult_histograms.png,Feature generation based on both variables marital-status and workclass seems to be promising.,536
+adult_histograms.png,Feature generation based on both variables capital-gain and age seems to be promising.,529
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and texture_mean seems to be promising.,11615
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and symmetry_se seems to be promising.,11671
+adult_histograms.png,Feature generation based on both variables age and relationship seems to be promising.,603
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_mean and radius_worst seems to be promising.,11677
+adult_histograms.png,Feature generation based on both variables age and education seems to be promising.,556
+adult_histograms.png,Feature generation based on both variables gender and hours-per-week seems to be promising.,672
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and area_se seems to be promising.,11655
+adult_histograms.png,Feature generation based on both variables workclass and hours-per-week seems to be promising.,664
+adult_histograms.png,Feature generation based on both variables relationship and educational-num seems to be promising.,573
+adult_histograms.png,Feature generation based on both variables race and gender seems to be promising.,635
+adult_histograms.png,Feature generation based on both variables marital-status and relationship seems to be promising.,608
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_se and area_se seems to be promising.,11653
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and area_se seems to be promising.,11652
+adult_histograms.png,Feature generation based on both variables age and hours-per-week seems to be promising.,663
+adult_histograms.png,Feature generation based on both variables gender and workclass seems to be promising.,540
+adult_histograms.png,Feature generation based on both variables relationship and capital-loss seems to be promising.,658
+Breast_Cancer_histograms.png,Feature generation based on both variables radius_worst and texture_se seems to be promising.,11638
+adult_histograms.png,Feature generation based on both variables educational-num and gender seems to be promising.,631
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and perimeter_worst seems to be promising.,11701
+adult_histograms.png,Feature generation based on both variables relationship and fnlwgt seems to be promising.,550
+adult_histograms.png,Feature generation based on both variables race and education seems to be promising.,562
+adult_histograms.png,Feature generation based on both variables age and educational-num seems to be promising.,567
+adult_histograms.png,Feature generation based on both variables workclass and capital-loss seems to be promising.,652
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and perimeter_se seems to be promising.,11646
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and perimeter_worst seems to be promising.,11697
+adult_histograms.png,Feature generation based on both variables relationship and capital-gain seems to be promising.,646
+Breast_Cancer_histograms.png,Feature generation based on both variables symmetry_se and radius_worst seems to be promising.,11683
+Breast_Cancer_histograms.png,Feature generation based on both variables perimeter_mean and smoothness_se seems to be promising.,11660
+Breast_Cancer_histograms.png,Feature generation based on both variables texture_se and radius_worst seems to be promising.,11679
+Breast_Cancer_histograms.png,Feature generation based on both variables smoothness_se and texture_worst seems to be promising.,11691
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11601
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of education seems to be promising.",408
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11541
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of capital-loss seems to be promising.",503
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11574
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of educational-num seems to be promising.",416
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of occupation seems to be promising.",447
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of hours-per-week seems to be promising.",518
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of age seems to be promising.",366
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of age seems to be promising.",376
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of hours-per-week seems to be promising.",514
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of hours-per-week seems to be promising.",509
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11600
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of gender seems to be promising.",477
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of capital-loss seems to be promising.",502
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of smoothness_se seems to be promising.",11575
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of marital-status seems to be promising.",431
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of educational-num seems to be promising.",422
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11592
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of age seems to be promising.",365
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of educational-num seems to be promising.",414
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of education seems to be promising.",409
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of symmetry_se seems to be promising.",11578
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of perimeter_se seems to be promising.",11559
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of symmetry_se seems to be promising.",11585
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of educational-num seems to be promising.",418
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of marital-status seems to be promising.",430
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of age seems to be promising.",370
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of relationship seems to be promising.",455
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11613
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of marital-status seems to be promising.",424
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of radius_worst seems to be promising.",11594
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of area_se seems to be promising.",11568
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of marital-status seems to be promising.",428
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of education seems to be promising.",405
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of fnlwgt seems to be promising.",398
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of fnlwgt seems to be promising.",394
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of capital-gain seems to be promising.",493
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of occupation seems to be promising.",436
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of hours-per-week seems to be promising.",510
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of capital-gain seems to be promising.",494
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of education seems to be promising.",410
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of education seems to be promising.",403
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11599
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of hours-per-week seems to be promising.",515
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of symmetry_se seems to be promising.",11586
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of age seems to be promising.",372
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11605
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of occupation seems to be promising.",445
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of relationship seems to be promising.",457
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of marital-status seems to be promising.",425
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of hours-per-week seems to be promising.",519
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of gender seems to be promising.",478
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11556
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of fnlwgt seems to be promising.",389
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11582
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11598
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of capital-loss seems to be promising.",498
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of fnlwgt seems to be promising.",399
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of fnlwgt seems to be promising.",390
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11526
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11527
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of fnlwgt seems to be promising.",396
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of radius_worst seems to be promising.",11595
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of texture_se seems to be promising.",11549
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of gender seems to be promising.",479
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of workclass seems to be promising.",384
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11529
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of educational-num seems to be promising.",417
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of occupation seems to be promising.",446
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of texture_se seems to be promising.",11548
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of education seems to be promising.",411
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of marital-status seems to be promising.",426
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11581
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of race seems to be promising.",467
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11554
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of educational-num seems to be promising.",423
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of capital-loss seems to be promising.",504
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of educational-num seems to be promising.",413
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of fnlwgt seems to be promising.",397
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of relationship seems to be promising.",456
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of race seems to be promising.",466
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of marital-status seems to be promising.",432
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of education seems to be promising.",404
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of smoothness_se seems to be promising.",11570
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of capital-gain seems to be promising.",489
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of gender seems to be promising.",473
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of perimeter_se seems to be promising.",11552
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of gender seems to be promising.",480
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of gender seems to be promising.",474
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of area_se seems to be promising.",11565
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of capital-gain seems to be promising.",485
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11555
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of age seems to be promising.",374
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of workclass seems to be promising.",386
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of capital-gain seems to be promising.",488
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of workclass seems to be promising.",377
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of capital-gain seems to be promising.",484
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of workclass seems to be promising.",378
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of perimeter_se seems to be promising.",11553
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11611
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of hours-per-week seems to be promising.",512
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of race seems to be promising.",462
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of capital-gain seems to be promising.",490
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of relationship seems to be promising.",451
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of texture_mean seems to be promising.",11530
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of race seems to be promising.",460
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of area_se seems to be promising.",11562
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of texture_worst seems to be promising.",11596
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of education seems to be promising.",406
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of perimeter_se seems to be promising.",11558
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of age seems to be promising.",368
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of capital-gain seems to be promising.",492
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of capital-loss seems to be promising.",505
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of age seems to be promising.",375
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of texture_se seems to be promising.",11543
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of relationship seems to be promising.",458
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of race seems to be promising.",464
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of educational-num seems to be promising.",421
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of gender seems to be promising.",476
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of texture_mean seems to be promising.",11532
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11572
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11536
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of fnlwgt seems to be promising.",393
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of educational-num seems to be promising.",419
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of fnlwgt seems to be promising.",400
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of texture_worst seems to be promising.",11604
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of hours-per-week seems to be promising.",508
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of fnlwgt seems to be promising.",395
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of gender seems to be promising.",475
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11580
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11535
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of area_se seems to be promising.",11561
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of occupation seems to be promising.",441
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of workclass seems to be promising.",385
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of gender seems to be promising.",482
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of age seems to be promising.",369
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of age seems to be promising.",371
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of symmetry_se seems to be promising.",11583
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of workclass seems to be promising.",387
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11612
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of workclass seems to be promising.",379
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of fnlwgt seems to be promising.",391
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11591
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of education seems to be promising.",407
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of age seems to be promising.",373
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of gender seems to be promising.",483
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of occupation seems to be promising.",443
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11590
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of area_se seems to be promising.",11566
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of marital-status seems to be promising.",434
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of workclass seems to be promising.",380
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11538
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of gender seems to be promising.",472
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of hours-per-week seems to be promising.",517
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11534
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of relationship seems to be promising.",459
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of capital-gain seems to be promising.",487
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of texture_mean seems to be promising.",11524
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of area_se seems to be promising.",11567
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of texture_worst seems to be promising.",11603
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11606
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11610
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of capital-loss seems to be promising.",500
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of capital-loss seems to be promising.",506
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of relationship seems to be promising.",453
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of smoothness_se seems to be promising.",11576
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of occupation seems to be promising.",439
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of education seems to be promising.",402
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of marital-status seems to be promising.",433
+adult_histograms.png,"Feature generation based on the use of variable race wouldn’t be useful, but the use of hours-per-week seems to be promising.",516
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11589
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of capital-loss seems to be promising.",496
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of race seems to be promising.",471
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of fnlwgt seems to be promising.",392
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of education seems to be promising.",401
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of texture_se seems to be promising.",11546
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of capital-gain seems to be promising.",486
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of occupation seems to be promising.",440
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11525
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of smoothness_se seems to be promising.",11577
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of symmetry_se seems to be promising.",11579
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_worst wouldn’t be useful, but the use of texture_se seems to be promising.",11550
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of marital-status seems to be promising.",427
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of race seems to be promising.",461
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of perimeter_se seems to be promising.",11557
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of relationship seems to be promising.",448
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of educational-num seems to be promising.",420
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of occupation seems to be promising.",438
+adult_histograms.png,"Feature generation based on the use of variable educational-num wouldn’t be useful, but the use of relationship seems to be promising.",452
+adult_histograms.png,"Feature generation based on the use of variable age wouldn’t be useful, but the use of educational-num seems to be promising.",412
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of texture_se seems to be promising.",11547
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of occupation seems to be promising.",437
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of race seems to be promising.",469
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of capital-loss seems to be promising.",497
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of capital-loss seems to be promising.",507
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11573
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of marital-status seems to be promising.",435
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of occupation seems to be promising.",442
+adult_histograms.png,"Feature generation based on the use of variable capital-gain wouldn’t be useful, but the use of gender seems to be promising.",481
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of capital-loss seems to be promising.",499
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of hours-per-week seems to be promising.",511
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11608
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of texture_se seems to be promising.",11544
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of texture_se seems to be promising.",11542
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of texture_se seems to be promising.",11545
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11607
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of radius_worst seems to be promising.",11588
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of workclass seems to be promising.",382
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of occupation seems to be promising.",444
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of workclass seems to be promising.",383
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of educational-num seems to be promising.",415
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of texture_mean seems to be promising.",11531
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of marital-status seems to be promising.",429
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of perimeter_se seems to be promising.",11551
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of area_se seems to be promising.",11560
+adult_histograms.png,"Feature generation based on the use of variable occupation wouldn’t be useful, but the use of relationship seems to be promising.",454
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of texture_mean seems to be promising.",11528
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of radius_worst seems to be promising.",11587
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11533
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of age seems to be promising.",367
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of race seems to be promising.",465
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of texture_worst seems to be promising.",11602
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of area_se seems to be promising.",11564
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_se wouldn’t be useful, but the use of area_se seems to be promising.",11563
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of symmetry_se seems to be promising.",11584
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable smoothness_se wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11537
+adult_histograms.png,"Feature generation based on the use of variable education wouldn’t be useful, but the use of race seems to be promising.",463
+adult_histograms.png,"Feature generation based on the use of variable fnlwgt wouldn’t be useful, but the use of relationship seems to be promising.",450
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of workclass seems to be promising.",388
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of capital-loss seems to be promising.",501
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_mean wouldn’t be useful, but the use of smoothness_se seems to be promising.",11569
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of workclass seems to be promising.",381
+adult_histograms.png,"Feature generation based on the use of variable relationship wouldn’t be useful, but the use of capital-gain seems to be promising.",491
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_se wouldn’t be useful, but the use of smoothness_se seems to be promising.",11571
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable area_se wouldn’t be useful, but the use of perimeter_worst seems to be promising.",11609
+adult_histograms.png,"Feature generation based on the use of variable marital-status wouldn’t be useful, but the use of hours-per-week seems to be promising.",513
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable radius_worst wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11539
+adult_histograms.png,"Feature generation based on the use of variable capital-loss wouldn’t be useful, but the use of race seems to be promising.",470
+adult_histograms.png,"Feature generation based on the use of variable workclass wouldn’t be useful, but the use of relationship seems to be promising.",449
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable texture_worst wouldn’t be useful, but the use of perimeter_mean seems to be promising.",11540
+adult_histograms.png,"Feature generation based on the use of variable hours-per-week wouldn’t be useful, but the use of capital-gain seems to be promising.",495
+adult_histograms.png,"Feature generation based on the use of variable gender wouldn’t be useful, but the use of race seems to be promising.",468
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable perimeter_mean wouldn’t be useful, but the use of texture_worst seems to be promising.",11597
+Breast_Cancer_histograms.png,"Feature generation based on the use of variable symmetry_se wouldn’t be useful, but the use of radius_worst seems to be promising.",11593
+heart_histograms.png,"Given the usual semantics of ca variable, dummification would have been a better codification.",2951
+Breast_Cancer_histograms.png,"Given the usual semantics of perimeter_mean variable, dummification would have been a better codification.",11515
+vehicle_histograms.png,"Given the usual semantics of RADIUS RATIO variable, dummification would have been a better codification.",4727
+heart_histograms.png,"Given the usual semantics of age variable, dummification would have been a better codification.",2940
+adult_histograms.png,"Given the usual semantics of capital-loss variable, dummification would have been a better codification.",363
+heart_histograms.png,"Given the usual semantics of oldpeak variable, dummification would have been a better codification.",2949
+BankNoteAuthentication_histograms.png,"Given the usual semantics of entropy variable, dummification would have been a better codification.",9939
+Wine_histograms.png,"Given the usual semantics of Flavanoids variable, dummification would have been a better codification.",8460
+vehicle_histograms.png,"Given the usual semantics of DISTANCE CIRCULARITY variable, dummification would have been a better codification.",4726
+Wine_histograms.png,"Given the usual semantics of Color intensity variable, dummification would have been a better codification.",8463
+vehicle_histograms.png,"Given the usual semantics of CIRCULARITY variable, dummification would have been a better codification.",4725
+BankNoteAuthentication_histograms.png,"Given the usual semantics of variance variable, dummification would have been a better codification.",9936
+Churn_Modelling_histograms.png,"Given the usual semantics of Tenure variable, dummification would have been a better codification.",1958
+heart_histograms.png,"Given the usual semantics of thalach variable, dummification would have been a better codification.",2947
+diabetes_histograms.png,"Given the usual semantics of BMI variable, dummification would have been a better codification.",10495
+Breast_Cancer_histograms.png,"Given the usual semantics of smoothness_se variable, dummification would have been a better codification.",11519
+Churn_Modelling_histograms.png,"Given the usual semantics of Balance variable, dummification would have been a better codification.",1959
+diabetes_histograms.png,"Given the usual semantics of Age variable, dummification would have been a better codification.",10497
+diabetes_histograms.png,"Given the usual semantics of DiabetesPedigreeFunction variable, dummification would have been a better codification.",10496
+Churn_Modelling_histograms.png,"Given the usual semantics of Age variable, dummification would have been a better codification.",1957
+vehicle_histograms.png,"Given the usual semantics of COMPACTNESS variable, dummification would have been a better codification.",4724
+heart_histograms.png,"Given the usual semantics of sex variable, dummification would have been a better codification.",2941
+heart_histograms.png,"Given the usual semantics of fbs variable, dummification would have been a better codification.",2945
+vehicle_histograms.png,"Given the usual semantics of ELONGATEDNESS variable, dummification would have been a better codification.",4731
+Wine_histograms.png,"Given the usual semantics of Alcalinity of ash variable, dummification would have been a better codification.",8458
+vehicle_histograms.png,"Given the usual semantics of LENGTHRECTANGULAR variable, dummification would have been a better codification.",4733
+Iris_histograms.png,"Given the usual semantics of PetalWidthCm variable, dummification would have been a better codification.",7810
+adult_histograms.png,"Given the usual semantics of capital-gain variable, dummification would have been a better codification.",362
+Iris_histograms.png,"Given the usual semantics of SepalWidthCm variable, dummification would have been a better codification.",7808
+Breast_Cancer_histograms.png,"Given the usual semantics of texture_mean variable, dummification would have been a better codification.",11514
+Breast_Cancer_histograms.png,"Given the usual semantics of perimeter_se variable, dummification would have been a better codification.",11517
+Churn_Modelling_histograms.png,"Given the usual semantics of HasCrCard variable, dummification would have been a better codification.",1961
+vehicle_histograms.png,"Given the usual semantics of PR AXIS ASPECT RATIO variable, dummification would have been a better codification.",4728
+Churn_Modelling_histograms.png,"Given the usual semantics of NumOfProducts variable, dummification would have been a better codification.",1960
+vehicle_histograms.png,"Given the usual semantics of SCATTER RATIO variable, dummification would have been a better codification.",4730
+adult_histograms.png,"Given the usual semantics of marital-status variable, dummification would have been a better codification.",357
+Breast_Cancer_histograms.png,"Given the usual semantics of texture_worst variable, dummification would have been a better codification.",11522
+Churn_Modelling_histograms.png,"Given the usual semantics of IsActiveMember variable, dummification would have been a better codification.",1962
+Churn_Modelling_histograms.png,"Given the usual semantics of EstimatedSalary variable, dummification would have been a better codification.",1963
+adult_histograms.png,"Given the usual semantics of education variable, dummification would have been a better codification.",355
+adult_histograms.png,"Given the usual semantics of workclass variable, dummification would have been a better codification.",353
+Breast_Cancer_histograms.png,"Given the usual semantics of radius_worst variable, dummification would have been a better codification.",11521
+adult_histograms.png,"Given the usual semantics of educational-num variable, dummification would have been a better codification.",356
+adult_histograms.png,"Given the usual semantics of occupation variable, dummification would have been a better codification.",358
+Breast_Cancer_histograms.png,"Given the usual semantics of perimeter_worst variable, dummification would have been a better codification.",11523
+heart_histograms.png,"Given the usual semantics of slope variable, dummification would have been a better codification.",2950
+vehicle_histograms.png,"Given the usual semantics of HOLLOWS RATIO variable, dummification would have been a better codification.",4741
+heart_histograms.png,"Given the usual semantics of chol variable, dummification would have been a better codification.",2944
+Wine_histograms.png,"Given the usual semantics of Total phenols variable, dummification would have been a better codification.",8459
+heart_histograms.png,"Given the usual semantics of restecg variable, dummification would have been a better codification.",2946
+heart_histograms.png,"Given the usual semantics of exang variable, dummification would have been a better codification.",2948
+Churn_Modelling_histograms.png,"Given the usual semantics of Gender variable, dummification would have been a better codification.",1956
+heart_histograms.png,"Given the usual semantics of thal variable, dummification would have been a better codification.",2952
+Wine_histograms.png,"Given the usual semantics of Ash variable, dummification would have been a better codification.",8457
+vehicle_histograms.png,"Given the usual semantics of MAJORVARIANCE variable, dummification would have been a better codification.",4734
+vehicle_histograms.png,"Given the usual semantics of MAJORSKEWNESS variable, dummification would have been a better codification.",4737
+heart_histograms.png,"Given the usual semantics of trestbps variable, dummification would have been a better codification.",2943
+vehicle_histograms.png,"Given the usual semantics of PR AXISRECTANGULAR variable, dummification would have been a better codification.",4732
+Wine_histograms.png,"Given the usual semantics of Proanthocyanins variable, dummification would have been a better codification.",8462
+adult_histograms.png,"Given the usual semantics of relationship variable, dummification would have been a better codification.",359
+Wine_histograms.png,"Given the usual semantics of OD280-OD315 of diluted wines variable, dummification would have been a better codification.",8465
+Churn_Modelling_histograms.png,"Given the usual semantics of CreditScore variable, dummification would have been a better codification.",1954
+Breast_Cancer_histograms.png,"Given the usual semantics of symmetry_se variable, dummification would have been a better codification.",11520
+heart_histograms.png,"Given the usual semantics of cp variable, dummification would have been a better codification.",2942
+Wine_histograms.png,"Given the usual semantics of Alcohol variable, dummification would have been a better codification.",8455
+vehicle_histograms.png,"Given the usual semantics of MAJORKURTOSIS variable, dummification would have been a better codification.",4740
+BankNoteAuthentication_histograms.png,"Given the usual semantics of curtosis variable, dummification would have been a better codification.",9938
+vehicle_histograms.png,"Given the usual semantics of GYRATIONRADIUS variable, dummification would have been a better codification.",4736
+adult_histograms.png,"Given the usual semantics of fnlwgt variable, dummification would have been a better codification.",354
+BankNoteAuthentication_histograms.png,"Given the usual semantics of skewness variable, dummification would have been a better codification.",9937
+Iris_histograms.png,"Given the usual semantics of PetalLengthCm variable, dummification would have been a better codification.",7809
+diabetes_histograms.png,"Given the usual semantics of BloodPressure variable, dummification would have been a better codification.",10492
+adult_histograms.png,"Given the usual semantics of hours-per-week variable, dummification would have been a better codification.",364
+Wine_histograms.png,"Given the usual semantics of Malic acid variable, dummification would have been a better codification.",8456
+Wine_histograms.png,"Given the usual semantics of Hue variable, dummification would have been a better codification.",8464
+diabetes_histograms.png,"Given the usual semantics of Insulin variable, dummification would have been a better codification.",10494
+diabetes_histograms.png,"Given the usual semantics of Glucose variable, dummification would have been a better codification.",10491
+adult_histograms.png,"Given the usual semantics of age variable, dummification would have been a better codification.",352
+Iris_histograms.png,"Given the usual semantics of SepalLengthCm variable, dummification would have been a better codification.",7807
+Breast_Cancer_histograms.png,"Given the usual semantics of texture_se variable, dummification would have been a better codification.",11516
+Wine_histograms.png,"Given the usual semantics of Nonflavanoid phenols variable, dummification would have been a better codification.",8461
+vehicle_histograms.png,"Given the usual semantics of MAX LENGTH ASPECT RATIO variable, dummification would have been a better codification.",4729
+vehicle_histograms.png,"Given the usual semantics of MINORKURTOSIS variable, dummification would have been a better codification.",4739
+Churn_Modelling_histograms.png,"Given the usual semantics of Geography variable, dummification would have been a better codification.",1955
+diabetes_histograms.png,"Given the usual semantics of Pregnancies variable, dummification would have been a better codification.",10490
+adult_histograms.png,"Given the usual semantics of gender variable, dummification would have been a better codification.",361
+vehicle_histograms.png,"Given the usual semantics of MINORSKEWNESS variable, dummification would have been a better codification.",4738
+vehicle_histograms.png,"Given the usual semantics of MINORVARIANCE variable, dummification would have been a better codification.",4735
+Breast_Cancer_histograms.png,"Given the usual semantics of area_se variable, dummification would have been a better codification.",11518
+diabetes_histograms.png,"Given the usual semantics of SkinThickness variable, dummification would have been a better codification.",10493
+adult_histograms.png,"Given the usual semantics of race variable, dummification would have been a better codification.",360
+adult_histograms.png,It is better to drop the variable education than removing all records with missing values.,342
+Breast_Cancer_histograms.png,It is better to drop the variable perimeter_worst than removing all records with missing values.,11513
+adult_histograms.png,It is better to drop the variable age than removing all records with missing values.,339
+adult_histograms.png,It is better to drop the variable capital-gain than removing all records with missing values.,349
+adult_histograms.png,It is better to drop the variable gender than removing all records with missing values.,348
+adult_histograms.png,It is better to drop the variable relationship than removing all records with missing values.,346
+adult_histograms.png,It is better to drop the variable fnlwgt than removing all records with missing values.,341
+adult_histograms.png,It is better to drop the variable marital-status than removing all records with missing values.,344
+adult_histograms.png,It is better to drop the variable workclass than removing all records with missing values.,340
+adult_histograms.png,It is better to drop the variable race than removing all records with missing values.,347
+adult_histograms.png,It is better to drop the variable hours-per-week than removing all records with missing values.,351
+Breast_Cancer_histograms.png,It is better to drop the variable area_se than removing all records with missing values.,11508
+Breast_Cancer_histograms.png,It is better to drop the variable smoothness_se than removing all records with missing values.,11509
+Breast_Cancer_histograms.png,It is better to drop the variable symmetry_se than removing all records with missing values.,11510
+Breast_Cancer_histograms.png,It is better to drop the variable texture_mean than removing all records with missing values.,11504
+adult_histograms.png,It is better to drop the variable educational-num than removing all records with missing values.,343
+Breast_Cancer_histograms.png,It is better to drop the variable texture_worst than removing all records with missing values.,11512
+Breast_Cancer_histograms.png,It is better to drop the variable perimeter_mean than removing all records with missing values.,11505
+adult_histograms.png,It is better to drop the variable occupation than removing all records with missing values.,345
+Breast_Cancer_histograms.png,It is better to drop the variable perimeter_se than removing all records with missing values.,11507
+adult_histograms.png,It is better to drop the variable capital-loss than removing all records with missing values.,350
+Breast_Cancer_histograms.png,It is better to drop the variable radius_worst than removing all records with missing values.,11511
+Breast_Cancer_histograms.png,It is better to drop the variable texture_se than removing all records with missing values.,11506
+heart_histograms.png,"Not knowing the semantics of oldpeak variable, dummification could have been a more adequate codification.",2934
+vehicle_histograms.png,"Not knowing the semantics of MAJORSKEWNESS variable, dummification could have been a more adequate codification.",4717
+adult_histograms.png,"Not knowing the semantics of relationship variable, dummification could have been a more adequate codification.",331
+Churn_Modelling_histograms.png,"Not knowing the semantics of CreditScore variable, dummification could have been a more adequate codification.",1942
+heart_histograms.png,"Not knowing the semantics of thalach variable, dummification could have been a more adequate codification.",2932
+heart_histograms.png,"Not knowing the semantics of fbs variable, dummification could have been a more adequate codification.",2930
+Wine_histograms.png,"Not knowing the semantics of Total phenols variable, dummification could have been a more adequate codification.",8446
+adult_histograms.png,"Not knowing the semantics of education variable, dummification could have been a more adequate codification.",327
+diabetes_histograms.png,"Not knowing the semantics of Age variable, dummification could have been a more adequate codification.",10487
+adult_histograms.png,"Not knowing the semantics of hours-per-week variable, dummification could have been a more adequate codification.",336
+Churn_Modelling_histograms.png,"Not knowing the semantics of Balance variable, dummification could have been a more adequate codification.",1947
+heart_histograms.png,"Not knowing the semantics of sex variable, dummification could have been a more adequate codification.",2926
+vehicle_histograms.png,"Not knowing the semantics of RADIUS RATIO variable, dummification could have been a more adequate codification.",4707
+Breast_Cancer_histograms.png,"Not knowing the semantics of texture_mean variable, dummification could have been a more adequate codification.",11492
+BankNoteAuthentication_histograms.png,"Not knowing the semantics of curtosis variable, dummification could have been a more adequate codification.",9932
+Breast_Cancer_histograms.png,"Not knowing the semantics of symmetry_se variable, dummification could have been a more adequate codification.",11498
+heart_histograms.png,"Not knowing the semantics of thal variable, dummification could have been a more adequate codification.",2937
+vehicle_histograms.png,"Not knowing the semantics of MAJORKURTOSIS variable, dummification could have been a more adequate codification.",4720
+adult_histograms.png,"Not knowing the semantics of capital-loss variable, dummification could have been a more adequate codification.",335
+Wine_histograms.png,"Not knowing the semantics of Color intensity variable, dummification could have been a more adequate codification.",8450
+diabetes_histograms.png,"Not knowing the semantics of Insulin variable, dummification could have been a more adequate codification.",10484
+Wine_histograms.png,"Not knowing the semantics of Malic acid variable, dummification could have been a more adequate codification.",8443
+adult_histograms.png,"Not knowing the semantics of marital-status variable, dummification could have been a more adequate codification.",329
+vehicle_histograms.png,"Not knowing the semantics of CIRCULARITY variable, dummification could have been a more adequate codification.",4705
+vehicle_histograms.png,"Not knowing the semantics of PR AXIS ASPECT RATIO variable, dummification could have been a more adequate codification.",4708
+adult_histograms.png,"Not knowing the semantics of educational-num variable, dummification could have been a more adequate codification.",328
+Churn_Modelling_histograms.png,"Not knowing the semantics of Tenure variable, dummification could have been a more adequate codification.",1946
+vehicle_histograms.png,"Not knowing the semantics of HOLLOWS RATIO variable, dummification could have been a more adequate codification.",4721
+diabetes_histograms.png,"Not knowing the semantics of Pregnancies variable, dummification could have been a more adequate codification.",10480
+heart_histograms.png,"Not knowing the semantics of trestbps variable, dummification could have been a more adequate codification.",2928
+Iris_histograms.png,"Not knowing the semantics of PetalWidthCm variable, dummification could have been a more adequate codification.",7804
+diabetes_histograms.png,"Not knowing the semantics of SkinThickness variable, dummification could have been a more adequate codification.",10483
+adult_histograms.png,"Not knowing the semantics of fnlwgt variable, dummification could have been a more adequate codification.",326
+vehicle_histograms.png,"Not knowing the semantics of MINORVARIANCE variable, dummification could have been a more adequate codification.",4715
+Churn_Modelling_histograms.png,"Not knowing the semantics of NumOfProducts variable, dummification could have been a more adequate codification.",1948
+Breast_Cancer_histograms.png,"Not knowing the semantics of area_se variable, dummification could have been a more adequate codification.",11496
+vehicle_histograms.png,"Not knowing the semantics of SCATTER RATIO variable, dummification could have been a more adequate codification.",4710
+Wine_histograms.png,"Not knowing the semantics of Nonflavanoid phenols variable, dummification could have been a more adequate codification.",8448
+Breast_Cancer_histograms.png,"Not knowing the semantics of perimeter_worst variable, dummification could have been a more adequate codification.",11501
+diabetes_histograms.png,"Not knowing the semantics of DiabetesPedigreeFunction variable, dummification could have been a more adequate codification.",10486
+vehicle_histograms.png,"Not knowing the semantics of MAX LENGTH ASPECT RATIO variable, dummification could have been a more adequate codification.",4709
+Iris_histograms.png,"Not knowing the semantics of SepalWidthCm variable, dummification could have been a more adequate codification.",7802
+vehicle_histograms.png,"Not knowing the semantics of PR AXISRECTANGULAR variable, dummification could have been a more adequate codification.",4712
+adult_histograms.png,"Not knowing the semantics of occupation variable, dummification could have been a more adequate codification.",330
+Breast_Cancer_histograms.png,"Not knowing the semantics of smoothness_se variable, dummification could have been a more adequate codification.",11497
+Breast_Cancer_histograms.png,"Not knowing the semantics of texture_worst variable, dummification could have been a more adequate codification.",11500
+adult_histograms.png,"Not knowing the semantics of gender variable, dummification could have been a more adequate codification.",333
+BankNoteAuthentication_histograms.png,"Not knowing the semantics of skewness variable, dummification could have been a more adequate codification.",9931
+Churn_Modelling_histograms.png,"Not knowing the semantics of Geography variable, dummification could have been a more adequate codification.",1943
+Wine_histograms.png,"Not knowing the semantics of Proanthocyanins variable, dummification could have been a more adequate codification.",8449
+diabetes_histograms.png,"Not knowing the semantics of BMI variable, dummification could have been a more adequate codification.",10485
+adult_histograms.png,"Not knowing the semantics of age variable, dummification could have been a more adequate codification.",324
+Iris_histograms.png,"Not knowing the semantics of PetalLengthCm variable, dummification could have been a more adequate codification.",7803
+adult_histograms.png,"Not knowing the semantics of workclass variable, dummification could have been a more adequate codification.",325
+vehicle_histograms.png,"Not knowing the semantics of MAJORVARIANCE variable, dummification could have been a more adequate codification.",4714
+Wine_histograms.png,"Not knowing the semantics of Hue variable, dummification could have been a more adequate codification.",8451
+Iris_histograms.png,"Not knowing the semantics of SepalLengthCm variable, dummification could have been a more adequate codification.",7801
+BankNoteAuthentication_histograms.png,"Not knowing the semantics of variance variable, dummification could have been a more adequate codification.",9930
+vehicle_histograms.png,"Not knowing the semantics of GYRATIONRADIUS variable, dummification could have been a more adequate codification.",4716
+vehicle_histograms.png,"Not knowing the semantics of ELONGATEDNESS variable, dummification could have been a more adequate codification.",4711
+diabetes_histograms.png,"Not knowing the semantics of Glucose variable, dummification could have been a more adequate codification.",10481
+heart_histograms.png,"Not knowing the semantics of cp variable, dummification could have been a more adequate codification.",2927
+Breast_Cancer_histograms.png,"Not knowing the semantics of perimeter_se variable, dummification could have been a more adequate codification.",11495
+heart_histograms.png,"Not knowing the semantics of restecg variable, dummification could have been a more adequate codification.",2931
+vehicle_histograms.png,"Not knowing the semantics of MINORSKEWNESS variable, dummification could have been a more adequate codification.",4718
+vehicle_histograms.png,"Not knowing the semantics of DISTANCE CIRCULARITY variable, dummification could have been a more adequate codification.",4706
+Wine_histograms.png,"Not knowing the semantics of Alcalinity of ash variable, dummification could have been a more adequate codification.",8445
+Wine_histograms.png,"Not knowing the semantics of OD280-OD315 of diluted wines variable, dummification could have been a more adequate codification.",8452
+Churn_Modelling_histograms.png,"Not knowing the semantics of HasCrCard variable, dummification could have been a more adequate codification.",1949
+Breast_Cancer_histograms.png,"Not knowing the semantics of texture_se variable, dummification could have been a more adequate codification.",11494
+adult_histograms.png,"Not knowing the semantics of capital-gain variable, dummification could have been a more adequate codification.",334
+Churn_Modelling_histograms.png,"Not knowing the semantics of Age variable, dummification could have been a more adequate codification.",1945
+Breast_Cancer_histograms.png,"Not knowing the semantics of radius_worst variable, dummification could have been a more adequate codification.",11499
+vehicle_histograms.png,"Not knowing the semantics of MINORKURTOSIS variable, dummification could have been a more adequate codification.",4719
+diabetes_histograms.png,"Not knowing the semantics of BloodPressure variable, dummification could have been a more adequate codification.",10482
+Churn_Modelling_histograms.png,"Not knowing the semantics of IsActiveMember variable, dummification could have been a more adequate codification.",1950
+adult_histograms.png,"Not knowing the semantics of race variable, dummification could have been a more adequate codification.",332
+Churn_Modelling_histograms.png,"Not knowing the semantics of Gender variable, dummification could have been a more adequate codification.",1944
+heart_histograms.png,"Not knowing the semantics of exang variable, dummification could have been a more adequate codification.",2933
+heart_histograms.png,"Not knowing the semantics of age variable, dummification could have been a more adequate codification.",2925
+vehicle_histograms.png,"Not knowing the semantics of LENGTHRECTANGULAR variable, dummification could have been a more adequate codification.",4713
+Breast_Cancer_histograms.png,"Not knowing the semantics of perimeter_mean variable, dummification could have been a more adequate codification.",11493
+Wine_histograms.png,"Not knowing the semantics of Ash variable, dummification could have been a more adequate codification.",8444
+heart_histograms.png,"Not knowing the semantics of chol variable, dummification could have been a more adequate codification.",2929
+Wine_histograms.png,"Not knowing the semantics of Alcohol variable, dummification could have been a more adequate codification.",8442
+Churn_Modelling_histograms.png,"Not knowing the semantics of EstimatedSalary variable, dummification could have been a more adequate codification.",1951
+BankNoteAuthentication_histograms.png,"Not knowing the semantics of entropy variable, dummification could have been a more adequate codification.",9933
+vehicle_histograms.png,"Not knowing the semantics of COMPACTNESS variable, dummification could have been a more adequate codification.",4704
+Wine_histograms.png,"Not knowing the semantics of Flavanoids variable, dummification could have been a more adequate codification.",8447
+heart_histograms.png,"Not knowing the semantics of slope variable, dummification could have been a more adequate codification.",2935
+heart_histograms.png,"Not knowing the semantics of ca variable, dummification could have been a more adequate codification.",2936
+vehicle_correlation_heatmap.png,Removing variable target might improve the training of decision trees .,4703
+Wine_correlation_heatmap.png,Removing variable Alcalinity of ash might improve the training of decision trees .,8434
+vehicle_correlation_heatmap.png,Removing variable DISTANCE CIRCULARITY might improve the training of decision trees .,4687
+diabetes_correlation_heatmap.png,Removing variable BMI might improve the training of decision trees .,10477
+Iris_correlation_heatmap.png,Removing variable SepalLengthCm might improve the training of decision trees .,7797
+Churn_Modelling_correlation_heatmap.png,Removing variable CreditScore might improve the training of decision trees .,1936
+adult_correlation_heatmap.png,Removing variable age might improve the training of decision trees .,318
+adult_correlation_heatmap.png,Removing variable capital-loss might improve the training of decision trees .,322
+Wine_correlation_heatmap.png,Removing variable Class might improve the training of decision trees .,8430
+vehicle_correlation_heatmap.png,Removing variable MAJORKURTOSIS might improve the training of decision trees .,4701
+Wine_correlation_heatmap.png,Removing variable Malic acid might improve the training of decision trees .,8432
+vehicle_correlation_heatmap.png,Removing variable MAJORSKEWNESS might improve the training of decision trees .,4698
+vehicle_correlation_heatmap.png,Removing variable PR AXISRECTANGULAR might improve the training of decision trees .,4693
+Breast_Cancer_correlation_heatmap.png,Removing variable symmetry_se might improve the training of decision trees .,11488
+Breast_Cancer_correlation_heatmap.png,Removing variable perimeter_mean might improve the training of decision trees .,11483
+BankNoteAuthentication_correlation_heatmap.png,Removing variable curtosis might improve the training of decision trees .,9928
+BankNoteAuthentication_correlation_heatmap.png,Removing variable variance might improve the training of decision trees .,9926
+heart_correlation_heatmap.png,Removing variable trestbps might improve the training of decision trees .,2917
+vehicle_correlation_heatmap.png,Removing variable HOLLOWS RATIO might improve the training of decision trees .,4702
+diabetes_correlation_heatmap.png,Removing variable SkinThickness might improve the training of decision trees .,10475
+vehicle_correlation_heatmap.png,Removing variable MAJORVARIANCE might improve the training of decision trees .,4695
+Wine_correlation_heatmap.png,Removing variable Total phenols might improve the training of decision trees .,8435
+adult_correlation_heatmap.png,Removing variable capital-gain might improve the training of decision trees .,321
+Wine_correlation_heatmap.png,Removing variable OD280-OD315 of diluted wines might improve the training of decision trees .,8441
+diabetes_correlation_heatmap.png,Removing variable DiabetesPedigreeFunction might improve the training of decision trees .,10478
+heart_correlation_heatmap.png,Removing variable cp might improve the training of decision trees .,2916
+Iris_correlation_heatmap.png,Removing variable SepalWidthCm might improve the training of decision trees .,7798
+Iris_correlation_heatmap.png,Removing variable PetalLengthCm might improve the training of decision trees .,7799
+BankNoteAuthentication_correlation_heatmap.png,Removing variable skewness might improve the training of decision trees .,9927
+vehicle_correlation_heatmap.png,Removing variable CIRCULARITY might improve the training of decision trees .,4686
+vehicle_correlation_heatmap.png,Removing variable GYRATIONRADIUS might improve the training of decision trees .,4697
+BankNoteAuthentication_correlation_heatmap.png,Removing variable entropy might improve the training of decision trees .,9929
+adult_correlation_heatmap.png,Removing variable hours-per-week might improve the training of decision trees .,323
+Churn_Modelling_correlation_heatmap.png,Removing variable NumOfProducts might improve the training of decision trees .,1940
+Iris_correlation_heatmap.png,Removing variable PetalWidthCm might improve the training of decision trees .,7800
+Wine_correlation_heatmap.png,Removing variable Proanthocyanins might improve the training of decision trees .,8438
+Breast_Cancer_correlation_heatmap.png,Removing variable texture_mean might improve the training of decision trees .,11482
+heart_correlation_heatmap.png,Removing variable ca might improve the training of decision trees .,2923
+vehicle_correlation_heatmap.png,Removing variable COMPACTNESS might improve the training of decision trees .,4685
+heart_correlation_heatmap.png,Removing variable oldpeak might improve the training of decision trees .,2921
+Breast_Cancer_correlation_heatmap.png,Removing variable texture_worst might improve the training of decision trees .,11490
+vehicle_correlation_heatmap.png,Removing variable MINORSKEWNESS might improve the training of decision trees .,4699
+Breast_Cancer_correlation_heatmap.png,Removing variable texture_se might improve the training of decision trees .,11484
+Wine_correlation_heatmap.png,Removing variable Flavanoids might improve the training of decision trees .,8436
+vehicle_correlation_heatmap.png,Removing variable SCATTER RATIO might improve the training of decision trees .,4691
+Wine_correlation_heatmap.png,Removing variable Ash might improve the training of decision trees .,8433
+Breast_Cancer_correlation_heatmap.png,Removing variable smoothness_se might improve the training of decision trees .,11487
+heart_correlation_heatmap.png,Removing variable chol might improve the training of decision trees .,2918
+vehicle_correlation_heatmap.png,Removing variable LENGTHRECTANGULAR might improve the training of decision trees .,4694
+vehicle_correlation_heatmap.png,Removing variable RADIUS RATIO might improve the training of decision trees .,4688
+vehicle_correlation_heatmap.png,Removing variable ELONGATEDNESS might improve the training of decision trees .,4692
+vehicle_correlation_heatmap.png,Removing variable PR AXIS ASPECT RATIO might improve the training of decision trees .,4689
+diabetes_correlation_heatmap.png,Removing variable Pregnancies might improve the training of decision trees .,10472
+Wine_correlation_heatmap.png,Removing variable Nonflavanoid phenols might improve the training of decision trees .,8437
+heart_correlation_heatmap.png,Removing variable thal might improve the training of decision trees .,2924
+diabetes_correlation_heatmap.png,Removing variable BloodPressure might improve the training of decision trees .,10474
+vehicle_correlation_heatmap.png,Removing variable MAX LENGTH ASPECT RATIO might improve the training of decision trees .,4690
+Churn_Modelling_correlation_heatmap.png,Removing variable Balance might improve the training of decision trees .,1939
+heart_correlation_heatmap.png,Removing variable age might improve the training of decision trees .,2915
+adult_correlation_heatmap.png,Removing variable fnlwgt might improve the training of decision trees .,319
+Wine_correlation_heatmap.png,Removing variable Alcohol might improve the training of decision trees .,8431
+Breast_Cancer_correlation_heatmap.png,Removing variable radius_worst might improve the training of decision trees .,11489
+diabetes_correlation_heatmap.png,Removing variable Age might improve the training of decision trees .,10479
+Breast_Cancer_correlation_heatmap.png,Removing variable area_se might improve the training of decision trees .,11486
+Breast_Cancer_correlation_heatmap.png,Removing variable perimeter_se might improve the training of decision trees .,11485
+adult_correlation_heatmap.png,Removing variable educational-num might improve the training of decision trees .,320
+vehicle_correlation_heatmap.png,Removing variable MINORVARIANCE might improve the training of decision trees .,4696
+diabetes_correlation_heatmap.png,Removing variable Glucose might improve the training of decision trees .,10473
+Churn_Modelling_correlation_heatmap.png,Removing variable Age might improve the training of decision trees .,1937
+heart_correlation_heatmap.png,Removing variable thalach might improve the training of decision trees .,2920
+heart_correlation_heatmap.png,Removing variable restecg might improve the training of decision trees .,2919
+Churn_Modelling_correlation_heatmap.png,Removing variable EstimatedSalary might improve the training of decision trees .,1941
+Churn_Modelling_correlation_heatmap.png,Removing variable Tenure might improve the training of decision trees .,1938
+Breast_Cancer_correlation_heatmap.png,Removing variable perimeter_worst might improve the training of decision trees .,11491
+Wine_correlation_heatmap.png,Removing variable Hue might improve the training of decision trees .,8440
+heart_correlation_heatmap.png,Removing variable slope might improve the training of decision trees .,2922
+vehicle_correlation_heatmap.png,Removing variable MINORKURTOSIS might improve the training of decision trees .,4700
+Wine_correlation_heatmap.png,Removing variable Color intensity might improve the training of decision trees .,8439
+diabetes_correlation_heatmap.png,Removing variable Insulin might improve the training of decision trees .,10476
+Iris_pca.png,The first 4 principal components are enough for explaining half the data variance.,7795
+Wine_pca.png,The first 3 principal components are enough for explaining half the data variance.,8427
+Churn_Modelling_pca.png,The first 3 principal components are enough for explaining half the data variance.,1933
+BankNoteAuthentication_pca.png,The first 4 principal components are enough for explaining half the data variance.,9924
+BankNoteAuthentication_pca.png,The first 2 principal components are enough for explaining half the data variance.,9922
+Iris_pca.png,The first 3 principal components are enough for explaining half the data variance.,7794
+Breast_Cancer_pca.png,The first 3 principal components are enough for explaining half the data variance.,11479
+vehicle_pca.png,The first 2 principal components are enough for explaining half the data variance.,4681
+vehicle_pca.png,The first 4 principal components are enough for explaining half the data variance.,4683
+Wine_pca.png,The first 4 principal components are enough for explaining half the data variance.,8428
+adult_pca.png,The first 3 principal components are enough for explaining half the data variance.,315
+adult_pca.png,The first 2 principal components are enough for explaining half the data variance.,314
+Wine_pca.png,The first 2 principal components are enough for explaining half the data variance.,8426
+Breast_Cancer_pca.png,The first 2 principal components are enough for explaining half the data variance.,11478
+adult_pca.png,The first 4 principal components are enough for explaining half the data variance.,316
+diabetes_pca.png,The first 2 principal components are enough for explaining half the data variance.,10468
+BankNoteAuthentication_pca.png,The first 3 principal components are enough for explaining half the data variance.,9923
+heart_pca.png,The first 4 principal components are enough for explaining half the data variance.,2913
+Iris_pca.png,The first 2 principal components are enough for explaining half the data variance.,7793
+diabetes_pca.png,The first 3 principal components are enough for explaining half the data variance.,10469
+Churn_Modelling_pca.png,The first 2 principal components are enough for explaining half the data variance.,1932
+Churn_Modelling_pca.png,The first 4 principal components are enough for explaining half the data variance.,1934
+heart_pca.png,The first 2 principal components are enough for explaining half the data variance.,2911
+Breast_Cancer_pca.png,The first 4 principal components are enough for explaining half the data variance.,11480
+diabetes_pca.png,The first 4 principal components are enough for explaining half the data variance.,10470
+vehicle_pca.png,The first 3 principal components are enough for explaining half the data variance.,4682
+heart_pca.png,The first 3 principal components are enough for explaining half the data variance.,2912
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable fnlwgt previously than variable educational-num.,295
+BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable variance previously than variable skewness.,9913
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable RADIUS RATIO.,4402
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable Pregnancies.,10416
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BloodPressure previously than variable BMI.,10449
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAX LENGTH ASPECT RATIO.,4434
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable hours-per-week previously than variable fnlwgt.,293
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Alcalinity of ash.,8338
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable EstimatedSalary.,1928
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Insulin.,10446
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable perimeter_mean.,11405
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable smoothness_se.,11434
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MINORSKEWNESS.,4598
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable HOLLOWS RATIO.,4650
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable COMPACTNESS.,4344
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable radius_worst.,11459
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable PR AXIS ASPECT RATIO.,4414
+BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable skewness previously than variable curtosis.,9917
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable OD280-OD315 of diluted wines.,8418
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Flavanoids.,8360
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable target.,4679
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Tenure previously than variable EstimatedSalary.,1929
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Tenure previously than variable Balance.,1919
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable PR AXISRECTANGULAR.,4486
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Total phenols.,8359
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable capital-loss.,304
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable trestbps.,2843
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable SCATTER RATIO.,4447
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Malic acid.,8316
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MINORSKEWNESS.,4603
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable NumOfProducts previously than variable CreditScore.,1905
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Alcalinity of ash.,8347
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable restecg.,2864
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable CIRCULARITY.,4367
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MAJORKURTOSIS.,4636
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable Insulin.,10441
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Color intensity.,8400
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable GYRATIONRADIUS.,4555
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable target.,4677
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable cp.,2839
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Flavanoids.,8370
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable trestbps.,2840
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-gain previously than variable capital-loss.,307
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable CIRCULARITY.,4358
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MINORVARIANCE.,4546
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Balance.,1918
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Class previously than variable Nonflavanoid phenols.,8371
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable ca.,2899
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable Balance.,1921
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable Pregnancies.,10417
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable cp.,2838
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable PR AXISRECTANGULAR.,4488
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable texture_worst.,11462
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable perimeter_mean.,11401
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable target.,4676
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable Pregnancies.,10414
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable texture_worst.,11463
+Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalLengthCm previously than variable PetalWidthCm.,7790
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable BMI.,10448
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable DISTANCE CIRCULARITY.,4376
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable ELONGATEDNESS.,4477
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Ash.,8333
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable RADIUS RATIO.,4403
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MAJORSKEWNESS.,4580
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable area_se.,11432
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable PR AXIS ASPECT RATIO.,4422
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MAJORKURTOSIS.,4644
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable Tenure.,1916
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable MAJORSKEWNESS.,4583
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MINORSKEWNESS.,4597
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAJORKURTOSIS.,4633
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable COMPACTNESS.,4351
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable RADIUS RATIO.,4397
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable CIRCULARITY.,4359
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable smoothness_se.,11440
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Hue.,8408
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Hue.,8410
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Nonflavanoid phenols.,8373
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MAJORSKEWNESS.,4586
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MAJORKURTOSIS.,4639
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable BloodPressure.,10432
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable SCATTER RATIO.,4459
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable thal.,2903
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Alcohol.,8309
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable CIRCULARITY.,4362
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable MAJORVARIANCE.,4528
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable SCATTER RATIO.,4448
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable LENGTHRECTANGULAR.,4508
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable GYRATIONRADIUS.,4567
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MINORKURTOSIS.,4616
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MINORKURTOSIS.,4626
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Total phenols.,8353
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Class.,8296
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable HOLLOWS RATIO.,4651
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable SkinThickness.,10434
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable smoothness_se.,11439
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable radius_worst.,11455
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MINORVARIANCE.,4541
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable BloodPressure.,10427
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Hue.,8405
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable target.,4670
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MAJORVARIANCE.,4529
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Ash.,8331
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable PR AXIS ASPECT RATIO.,4420
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MINORVARIANCE.,4538
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Flavanoids.,8361
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable chol.,2850
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MAJORVARIANCE.,4523
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable target.,4671
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable perimeter_se.,11416
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable oldpeak.,2878
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable LENGTHRECTANGULAR.,4511
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_mean previously than variable symmetry_se.,11443
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable RADIUS RATIO.,4393
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Nonflavanoid phenols.,8375
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Color intensity.,8402
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable LENGTHRECTANGULAR.,4509
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable ELONGATEDNESS.,4475
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable DISTANCE CIRCULARITY.,4388
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Nonflavanoid phenols.,8374
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MINORSKEWNESS.,4591
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable LENGTHRECTANGULAR.,4517
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable perimeter_se.,11415
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MINORSKEWNESS.,4595
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable GYRATIONRADIUS.,4558
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable age.,2823
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable trestbps.,2847
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_se previously than variable texture_mean.,11389
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MINORKURTOSIS.,4617
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable COMPACTNESS.,4355
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable educational-num previously than variable hours-per-week.,311
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable COMPACTNESS.,4354
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable LENGTHRECTANGULAR.,4512
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable HOLLOWS RATIO.,4648
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable perimeter_worst.,11477
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable MAJORKURTOSIS.,4628
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable thalach.,2869
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable HOLLOWS RATIO.,4652
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable ca.,2897
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Alcohol.,8313
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MAJORVARIANCE.,4526
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Ash.,8330
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Flavanoids.,8363
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Alcohol.,8314
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable OD280-OD315 of diluted wines.,8422
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-loss previously than variable hours-per-week.,313
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable GYRATIONRADIUS.,4557
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MINORVARIANCE.,4553
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable COMPACTNESS.,4356
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable LENGTHRECTANGULAR.,4510
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable texture_mean.,11391
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAX LENGTH ASPECT RATIO previously than variable PR AXIS ASPECT RATIO.,4415
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable RADIUS RATIO.,4408
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable MAX LENGTH ASPECT RATIO.,4435
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Total phenols.,8352
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MINORKURTOSIS.,4622
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Alcohol.,8307
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable NumOfProducts.,1926
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable COMPACTNESS.,4349
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_mean previously than variable smoothness_se.,11433
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Total phenols.,8354
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable restecg.,2861
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MINORSKEWNESS.,4604
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-gain previously than variable age.,286
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAJORSKEWNESS.,4579
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable RADIUS RATIO.,4404
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable age.,2830
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable PR AXISRECTANGULAR.,4484
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable smoothness_se.,11441
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable MINORKURTOSIS.,4623
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable CreditScore.,1904
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable BloodPressure.,10426
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable SCATTER RATIO.,4450
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable Malic acid.,8319
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable texture_worst previously than variable texture_mean.,11395
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Alcalinity of ash.,8344
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable slope.,2888
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable chol.,2856
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable CIRCULARITY previously than variable LENGTHRECTANGULAR.,4502
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MAX LENGTH ASPECT RATIO.,4436
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable RADIUS RATIO.,4398
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable LENGTHRECTANGULAR.,4504
+Iris_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SepalLengthCm previously than variable PetalLengthCm.,7787
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_worst previously than variable texture_worst.,11468
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Ash.,8335
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable texture_se.,11410
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable PR AXIS ASPECT RATIO.,4413
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable PR AXIS ASPECT RATIO.,4419
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Proanthocyanins.,8391
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable age previously than variable hours-per-week.,309
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MAX LENGTH ASPECT RATIO.,4429
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable MAJORSKEWNESS.,4573
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MINORVARIANCE.,4554
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORKURTOSIS previously than variable MINORVARIANCE.,4551
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcalinity of ash previously than variable OD280-OD315 of diluted wines.,8419
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable slope.,2892
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SkinThickness previously than variable Glucose.,10421
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable RADIUS RATIO previously than variable COMPACTNESS.,4342
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable SCATTER RATIO.,4453
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Alcohol.,8310
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXIS ASPECT RATIO previously than variable MINORKURTOSIS.,4613
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable SCATTER RATIO.,4462
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ca previously than variable age.,2829
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Proanthocyanins.,8392
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable SCATTER RATIO.,4463
+BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable curtosis previously than variable entropy.,9921
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable COMPACTNESS.,4346
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Class.,8294
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable MINORSKEWNESS.,4599
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable cp previously than variable thal.,2904
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable PR AXIS ASPECT RATIO.,4426
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable CreditScore.,1902
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable perimeter_se previously than variable texture_mean.,11390
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MAJORVARIANCE.,4536
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable perimeter_se.,11421
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MINORVARIANCE.,4539
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable HOLLOWS RATIO previously than variable MINORKURTOSIS.,4625
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Color intensity.,8395
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable MAJORKURTOSIS.,4629
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable BMI.,10453
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable PR AXISRECTANGULAR previously than variable SCATTER RATIO.,4454
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable SCATTER RATIO.,4455
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable PR AXISRECTANGULAR.,4491
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DiabetesPedigreeFunction previously than variable SkinThickness.,10438
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable symmetry_se.,11447
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable smoothness_se previously than variable area_se.,11428
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable CIRCULARITY.,4374
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable ELONGATEDNESS previously than variable PR AXISRECTANGULAR.,4490
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Glucose previously than variable DiabetesPedigreeFunction.,10455
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable SCATTER RATIO.,4456
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable DISTANCE CIRCULARITY.,4385
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable Age.,10466
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Ash.,8332
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable BMI previously than variable Glucose.,10423
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable SCATTER RATIO previously than variable MAJORVARIANCE.,4525
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable EstimatedSalary.,1930
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable EstimatedSalary previously than variable Age.,1911
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Malic acid.,8324
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable MAX LENGTH ASPECT RATIO.,4446
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MAJORVARIANCE.,4531
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable thal.,2908
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable COMPACTNESS.,4341
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable COMPACTNESS.,4348
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable oldpeak.,2880
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Age previously than variable Tenure.,1913
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable HOLLOWS RATIO.,4655
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Malic acid.,8317
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable OD280-OD315 of diluted wines.,8417
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thalach previously than variable trestbps.,2844
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable COMPACTNESS.,4350
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable restecg previously than variable cp.,2834
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORVARIANCE previously than variable PR AXISRECTANGULAR.,4492
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable target.,4675
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable chol previously than variable thal.,2906
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable slope previously than variable age.,2828
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Hue.,8413
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Nonflavanoid phenols previously than variable Flavanoids.,8366
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable NumOfProducts previously than variable EstimatedSalary.,1931
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Hue.,8412
+adult_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable capital-loss previously than variable capital-gain.,302
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable OD280-OD315 of diluted wines previously than variable Hue.,8414
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Total phenols previously than variable Malic acid.,8320
+Churn_Modelling_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Balance previously than variable NumOfProducts.,1925
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable SCATTER RATIO.,4460
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable MINORVARIANCE.,4548
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORKURTOSIS previously than variable LENGTHRECTANGULAR.,4516
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable trestbps previously than variable chol.,2851
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable radius_worst previously than variable texture_se.,11412
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable COMPACTNESS.,4352
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable target previously than variable COMPACTNESS.,4357
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Flavanoids previously than variable Malic acid.,8321
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable oldpeak previously than variable ca.,2900
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable LENGTHRECTANGULAR previously than variable ELONGATEDNESS.,4473
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Alcohol previously than variable Total phenols.,8350
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORVARIANCE previously than variable MAX LENGTH ASPECT RATIO.,4439
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable COMPACTNESS previously than variable PR AXISRECTANGULAR.,4483
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Proanthocyanins previously than variable Alcalinity of ash.,8345
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Color intensity previously than variable Class.,8302
+heart_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable thal previously than variable slope.,2893
+Breast_Cancer_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable area_se previously than variable perimeter_worst.,11473
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Total phenols.,8351
+BankNoteAuthentication_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable entropy previously than variable curtosis.,9918
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Malic acid previously than variable Proanthocyanins.,8384
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MAJORSKEWNESS previously than variable MAJORKURTOSIS.,4640
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable MINORSKEWNESS previously than variable PR AXIS ASPECT RATIO.,4424
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Ash previously than variable Proanthocyanins.,8385
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable GYRATIONRADIUS previously than variable PR AXISRECTANGULAR.,4494
+vehicle_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable DISTANCE CIRCULARITY previously than variable target.,4665
+Wine_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Hue previously than variable Class.,8303
+diabetes_correlation_heatmap.png,There is evidence in favour for sequential backward selection to select variable Pregnancies previously than variable SkinThickness.,10433
+diabetes_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,10401
+diabetes_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,10394
+adult_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,283
+Iris_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,7762
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,9901
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,1875
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,1881
+heart_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,2810
+Iris_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,7776
+Iris_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,7777
+adult_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,268
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,1897
+Iris_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,7768
+Iris_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,7770
+vehicle_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,4321
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,9884
+diabetes_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,10403
+heart_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,2796
+Iris_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,7775
+vehicle_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,4327
+adult_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,258
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,11382
+Wine_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,8284
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,9888
+Wine_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,8269
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,9896
+vehicle_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,4316
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,1898
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,9891
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,9885
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,11364
+diabetes_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,10402
+adult_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,270
+Iris_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,7766
+Iris_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,7757
+adult_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,281
+vehicle_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,4330
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,1900
+Iris_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,7765
+vehicle_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,4332
+Wine_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,8271
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,1899
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,9889
+Wine_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,8287
+heart_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,2805
+adult_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,260
+vehicle_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,4313
+diabetes_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,10400
+Wine_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,8272
+diabetes_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,10404
+heart_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,2820
+vehicle_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,4334
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,11367
+adult_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,267
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,1888
+Wine_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,8292
+heart_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,2808
+diabetes_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,10385
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,1895
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,11384
+diabetes_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,10387
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,11362
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,1878
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,11377
+vehicle_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,4318
+vehicle_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,4319
+Wine_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,8285
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,11380
+heart_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,2814
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,9899
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,11373
+heart_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,2806
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,9903
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,1889
+diabetes_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,10388
+Wine_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,8278
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,1876
+adult_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,264
+Wine_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,8279
+diabetes_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,10396
+Iris_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,7759
+adult_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,263
+adult_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,257
+diabetes_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,10399
+Iris_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,7764
+diabetes_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,10392
+Wine_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,8282
+Iris_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,7767
+vehicle_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,4317
+heart_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,2809
+diabetes_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,10391
+Wine_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,8277
+vehicle_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,4324
+diabetes_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,10386
+diabetes_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,10390
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,1893
+diabetes_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,10408
+vehicle_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,4328
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,9904
+Wine_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,8283
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,9895
+diabetes_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,10410
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,9906
+Iris_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,7772
+adult_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,272
+vehicle_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,4339
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,1890
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,1896
+Wine_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,8289
+heart_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,2811
+Wine_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,8288
+diabetes_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,10411
+heart_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,2799
+heart_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,2800
+Iris_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,7754
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,9898
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,1879
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,11370
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,11381
+vehicle_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,4322
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,11366
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,1877
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,1886
+adult_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,279
+heart_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,2803
+adult_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,271
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,9894
+Iris_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,7779
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,11361
+diabetes_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,10395
+vehicle_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,4337
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,9890
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,9900
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,11371
+heart_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,2795
+vehicle_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,4331
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,11365
+Iris_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,7755
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,11374
+Iris_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,7760
+heart_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,2801
+Iris_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,7780
+diabetes_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,10397
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,11363
+heart_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,2818
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,11386
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,11368
+heart_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,2798
+diabetes_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,10393
+diabetes_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,10406
+adult_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,275
+adult_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,273
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,1891
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,9887
+vehicle_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,4320
+heart_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,2813
+Wine_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,8275
+vehicle_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,4314
+heart_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,2817
+heart_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,2812
+vehicle_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,4333
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,1887
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,9905
+vehicle_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,4336
+Iris_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,7758
+vehicle_pca.png,Using the first 3 principal components would imply an error between 15 and 25%.,4329
+Wine_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,8267
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,9893
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,11387
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,11369
+adult_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,278
+Iris_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,7756
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,11385
+adult_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,269
+Wine_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,8291
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,9908
+vehicle_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,4325
+adult_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,265
+heart_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,2804
+Iris_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,7774
+Wine_pca.png,Using the first 3 principal components would imply an error between 5 and 20%.,8268
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,11376
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,9886
+Iris_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,7778
+Wine_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,8281
+Wine_pca.png,Using the first 2 principal components would imply an error between 15 and 20%.,8273
+heart_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,2802
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,1901
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 5 and 25%.,11372
+diabetes_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,10405
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,9909
+heart_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,2815
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,9892
+adult_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,259
+BankNoteAuthentication_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,9897
+Iris_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,7763
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,1885
+vehicle_pca.png,Using the first 3 principal components would imply an error between 5 and 25%.,4323
+Churn_Modelling_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,1884
+heart_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,2797
+heart_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,2821
+Wine_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,8280
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,1880
+diabetes_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,10398
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 5 and 20%.,9883
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,1882
+Wine_pca.png,Using the first 2 principal components would imply an error between 10 and 20%.,8270
+diabetes_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,10389
+Wine_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,8290
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,1892
+diabetes_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,10407
+Churn_Modelling_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,1894
+vehicle_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,4338
+adult_pca.png,Using the first 4 principal components would imply an error between 10 and 20%.,262
+Iris_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,7771
+heart_pca.png,Using the first 2 principal components would imply an error between 10 and 30%.,2816
+vehicle_pca.png,Using the first 4 principal components would imply an error between 5 and 20%.,4315
+Wine_pca.png,Using the first 4 principal components would imply an error between 15 and 30%.,8293
+BankNoteAuthentication_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,9902
+adult_pca.png,Using the first 3 principal components would imply an error between 15 and 30%.,282
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,11378
+vehicle_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,4335
+adult_pca.png,Using the first 4 principal components would imply an error between 15 and 25%.,274
+Iris_pca.png,Using the first 2 principal components would imply an error between 15 and 25%.,7769
+diabetes_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,10409
+BankNoteAuthentication_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,9907
+adult_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,266
+adult_pca.png,Using the first 4 principal components would imply an error between 5 and 30%.,277
+vehicle_pca.png,Using the first 3 principal components would imply an error between 10 and 25%.,4326
+Breast_Cancer_pca.png,Using the first 4 principal components would imply an error between 10 and 25%.,11375
+Iris_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,7773
+heart_pca.png,Using the first 2 principal components would imply an error between 15 and 30%.,2819
+Breast_Cancer_pca.png,Using the first 3 principal components would imply an error between 10 and 30%.,11383
+Wine_pca.png,Using the first 2 principal components would imply an error between 5 and 25%.,8276
+adult_pca.png,Using the first 4 principal components would imply an error between 10 and 30%.,280
+Wine_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,8286
+adult_pca.png,Using the first 3 principal components would imply an error between 10 and 20%.,261
+Churn_Modelling_pca.png,Using the first 4 principal components would imply an error between 15 and 20%.,1883
+Iris_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,7761
+heart_pca.png,Using the first 2 principal components would imply an error between 10 and 25%.,2807
+Wine_pca.png,Using the first 3 principal components would imply an error between 15 and 20%.,8274
+Breast_Cancer_pca.png,Using the first 2 principal components would imply an error between 5 and 30%.,11379
+adult_pca.png,Using the first 3 principal components would imply an error between 5 and 30%.,276
+Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",8266
+Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",1871
+Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",7752
+diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",10384
+Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",8264
+Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",11355
+Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",1873
+adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",253
+Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",1874
+diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",10383
+BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",9882
+adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",252
+Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",8263
+heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",2791
+Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",11358
+Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",8262
+diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",10381
+Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",1870
+vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",4312
+diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",10382
+Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",8265
+vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",4307
+Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",11359
+Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",11357
+adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",251
+vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",4308
+Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",1872
+heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",2793
+Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",7749
+Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",11356
+vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",4311
+vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",4310
+Churn_Modelling_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",1869
+vehicle_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",4309
+diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",10380
+diabetes_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",10379
+Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",7750
+adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",255
+Breast_Cancer_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",11360
+BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",9878
+Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",7753
+heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",2789
+Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",7751
+adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",256
+Wine_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",8261
+Iris_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",7748
+adult_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",254
+heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",2792
+BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 9%.",9881
+BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 8%.",9880
+BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 5%.",9877
+heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 6%.",2790
+BankNoteAuthentication_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 7%.",9879
+heart_decision_tree.png,"A smaller tree would be delivered if we would apply post-pruning, accepting an accuracy reduction of 10%.",2794
+vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",4302
+Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",7747
+vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",4303
+adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",247
+Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",8259
+diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",10378
+diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",10376
+BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",9873
+heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",2785
+adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",250
+Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",8260
+BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",9872
+heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",2783
+BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",9874
+diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",10374
+Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",8256
+Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",1863
+Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",11351
+heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",2787
+BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",9876
+vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",4305
+heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",2786
+adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",248
+Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",11353
+heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",2788
+Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",1867
+vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",4306
+Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",8258
+Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",7744
+diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",10375
+vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",4304
+adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",245
+Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",1866
+Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",7746
+Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",8257
+adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",249
+BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",9875
+Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",11352
+BankNoteAuthentication_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",9871
+Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",1864
+Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",11349
+Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",11350
+Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",7743
+Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 5 nodes of depth is in overfitting.",1865
+Churn_Modelling_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",1868
+diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",10373
+diabetes_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 7 nodes of depth is in overfitting.",10377
+Breast_Cancer_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 8 nodes of depth is in overfitting.",11354
+vehicle_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",4301
+Wine_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",8255
+Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 6 nodes of depth is in overfitting.",7745
+heart_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",2784
+Iris_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 3 nodes of depth is in overfitting.",7742
+adult_overfitting_decision_tree.png,"According to the decision tree overfitting chart, the tree with 4 nodes of depth is in overfitting.",246
+Churn_Modelling_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",1862
+Iris_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",7740
+Breast_Cancer_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",11347
+vehicle_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",4299
+Wine_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",8254
+BankNoteAuthentication_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",9869
+Breast_Cancer_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",11348
+heart_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",2782
+Churn_Modelling_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",1861
+diabetes_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",10371
+adult_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",244
+Wine_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",8253
+vehicle_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",4300
+heart_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",2781
+adult_decision_tree.png,"As reported in the tree, the number of False Positive is smaller than the number of False Negatives.",243
+diabetes_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",10372
+BankNoteAuthentication_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",9870
+Iris_decision_tree.png,"As reported in the tree, the number of False Positive is bigger than the number of False Negatives.",7741
+Wine_overfitting_knn.png,KNN is in overfitting for k larger than 7.,8250
+diabetes_overfitting_knn.png,KNN is in overfitting for k less than 6.,10365
+Wine_overfitting_knn.png,KNN is in overfitting for k larger than 4.,8244
+diabetes_overfitting_knn.png,KNN is in overfitting for k less than 2.,10357
+adult_overfitting_knn.png,KNN is in overfitting for k less than 2.,229
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 6.,9864
+diabetes_overfitting_knn.png,KNN is in overfitting for k less than 4.,10361
+diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 7.,10368
+adult_overfitting_knn.png,KNN is in overfitting for k larger than 7.,240
+adult_overfitting_knn.png,KNN is in overfitting for k larger than 2.,230
+heart_overfitting_knn.png,KNN is in overfitting for k less than 7.,2777
+vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 2.,4286
+Iris_overfitting_knn.png,KNN is in overfitting for k less than 4.,7730
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 5.,11340
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 8.,11345
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 6.,11342
+vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 6.,4294
+Iris_overfitting_knn.png,KNN is in overfitting for k larger than 2.,7727
+vehicle_overfitting_knn.png,KNN is in overfitting for k less than 3.,4287
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 5.,9861
+heart_overfitting_knn.png,KNN is in overfitting for k larger than 3.,2770
+Iris_overfitting_knn.png,KNN is in overfitting for k larger than 3.,7729
+adult_overfitting_knn.png,KNN is in overfitting for k less than 6.,237
+vehicle_overfitting_knn.png,KNN is in overfitting for k less than 4.,4289
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 2.,9856
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 8.,11346
+diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 6.,10366
+vehicle_overfitting_knn.png,KNN is in overfitting for k less than 7.,4295
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 3.,1850
+adult_overfitting_knn.png,KNN is in overfitting for k larger than 6.,238
+Wine_overfitting_knn.png,KNN is in overfitting for k less than 2.,8239
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 7.,11343
+adult_overfitting_knn.png,KNN is in overfitting for k less than 7.,239
+Wine_overfitting_knn.png,KNN is in overfitting for k less than 4.,8243
+Iris_overfitting_knn.png,KNN is in overfitting for k less than 6.,7734
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 6.,1855
+heart_overfitting_knn.png,KNN is in overfitting for k larger than 6.,2776
+Iris_overfitting_knn.png,KNN is in overfitting for k larger than 5.,7733
+heart_overfitting_knn.png,KNN is in overfitting for k less than 6.,2775
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 8.,9867
+Iris_overfitting_knn.png,KNN is in overfitting for k less than 7.,7736
+diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 5.,10364
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 7.,11344
+Wine_overfitting_knn.png,KNN is in overfitting for k less than 7.,8249
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 6.,11341
+heart_overfitting_knn.png,KNN is in overfitting for k larger than 2.,2768
+vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 5.,4292
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 3.,9857
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 6.,1856
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 4.,11337
+vehicle_overfitting_knn.png,KNN is in overfitting for k less than 8.,4297
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 2.,11334
+adult_overfitting_knn.png,KNN is in overfitting for k less than 3.,231
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 3.,11336
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k larger than 4.,11338
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 5.,9862
+adult_overfitting_knn.png,KNN is in overfitting for k larger than 5.,236
+diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 4.,10362
+heart_overfitting_knn.png,KNN is in overfitting for k less than 2.,2767
+heart_overfitting_knn.png,KNN is in overfitting for k larger than 4.,2772
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 6.,9863
+Wine_overfitting_knn.png,KNN is in overfitting for k larger than 5.,8246
+Iris_overfitting_knn.png,KNN is in overfitting for k less than 3.,7728
+Wine_overfitting_knn.png,KNN is in overfitting for k larger than 8.,8252
+vehicle_overfitting_knn.png,KNN is in overfitting for k less than 6.,4293
+Wine_overfitting_knn.png,KNN is in overfitting for k less than 6.,8247
+vehicle_overfitting_knn.png,KNN is in overfitting for k less than 5.,4291
+Iris_overfitting_knn.png,KNN is in overfitting for k less than 2.,7726
+Wine_overfitting_knn.png,KNN is in overfitting for k larger than 3.,8242
+Wine_overfitting_knn.png,KNN is in overfitting for k less than 8.,8251
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 7.,9865
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 8.,1859
+diabetes_overfitting_knn.png,KNN is in overfitting for k less than 8.,10369
+adult_overfitting_knn.png,KNN is in overfitting for k larger than 3.,232
+adult_overfitting_knn.png,KNN is in overfitting for k less than 5.,235
+Wine_overfitting_knn.png,KNN is in overfitting for k less than 3.,8241
+Iris_overfitting_knn.png,KNN is in overfitting for k less than 5.,7732
+Wine_overfitting_knn.png,KNN is in overfitting for k larger than 2.,8240
+heart_overfitting_knn.png,KNN is in overfitting for k less than 5.,2773
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 7.,1857
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 3.,9858
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 2.,11333
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 4.,9859
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k less than 2.,9855
+diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 3.,10360
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 4.,1851
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 3.,1849
+adult_overfitting_knn.png,KNN is in overfitting for k larger than 8.,242
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 5.,11339
+Iris_overfitting_knn.png,KNN is in overfitting for k larger than 8.,7739
+adult_overfitting_knn.png,KNN is in overfitting for k less than 4.,233
+heart_overfitting_knn.png,KNN is in overfitting for k larger than 8.,2780
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 4.,1852
+heart_overfitting_knn.png,KNN is in overfitting for k less than 3.,2769
+Iris_overfitting_knn.png,KNN is in overfitting for k larger than 6.,7735
+vehicle_overfitting_knn.png,KNN is in overfitting for k less than 2.,4285
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 8.,9868
+vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 8.,4298
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 2.,1848
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 7.,9866
+diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 8.,10370
+vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 4.,4290
+diabetes_overfitting_knn.png,KNN is in overfitting for k less than 5.,10363
+vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 3.,4288
+Iris_overfitting_knn.png,KNN is in overfitting for k larger than 4.,7731
+BankNoteAuthentication_overfitting_knn.png,KNN is in overfitting for k larger than 4.,9860
+heart_overfitting_knn.png,KNN is in overfitting for k larger than 5.,2774
+heart_overfitting_knn.png,KNN is in overfitting for k less than 8.,2779
+Wine_overfitting_knn.png,KNN is in overfitting for k larger than 6.,8248
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 8.,1860
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 5.,1854
+diabetes_overfitting_knn.png,KNN is in overfitting for k less than 3.,10359
+adult_overfitting_knn.png,KNN is in overfitting for k larger than 4.,234
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k larger than 7.,1858
+adult_overfitting_knn.png,KNN is in overfitting for k less than 8.,241
+diabetes_overfitting_knn.png,KNN is in overfitting for k larger than 2.,10358
+Iris_overfitting_knn.png,KNN is in overfitting for k less than 8.,7738
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 5.,1853
+vehicle_overfitting_knn.png,KNN is in overfitting for k larger than 7.,4296
+diabetes_overfitting_knn.png,KNN is in overfitting for k less than 7.,10367
+Breast_Cancer_overfitting_knn.png,KNN is in overfitting for k less than 3.,11335
+heart_overfitting_knn.png,KNN is in overfitting for k less than 4.,2771
+Churn_Modelling_overfitting_knn.png,KNN is in overfitting for k less than 2.,1847
+Iris_overfitting_knn.png,KNN is in overfitting for k larger than 7.,7737
+heart_overfitting_knn.png,KNN is in overfitting for k larger than 7.,2778
+Wine_overfitting_knn.png,KNN is in overfitting for k less than 5.,8245
+heart_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,2764
+Wine_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,8235
+Churn_Modelling_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,1844
+heart_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,2766
+adult_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,224
+Iris_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,7717
+adult_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,222
+BankNoteAuthentication_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,9852
+Breast_Cancer_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,11323
+heart_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,2759
+Breast_Cancer_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,11330
+Churn_Modelling_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,1845
+heart_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,2760
+vehicle_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,4277
+heart_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,2765
+BankNoteAuthentication_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,9848
+Breast_Cancer_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,11327
+Churn_Modelling_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,1842
+vehicle_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,4280
+adult_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,221
+Iris_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,7725
+Iris_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,7722
+Churn_Modelling_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,1838
+Churn_Modelling_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,1840
+heart_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,2757
+diabetes_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,10351
+BankNoteAuthentication_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,9853
+diabetes_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,10353
+Churn_Modelling_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,1837
+Iris_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,7723
+BankNoteAuthentication_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,9850
+heart_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,2763
+adult_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,223
+Wine_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,8236
+adult_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,219
+heart_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,2761
+diabetes_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,10347
+Iris_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,7721
+Churn_Modelling_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,1846
+heart_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,2762
+heart_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,2758
+diabetes_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,10349
+adult_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,225
+Breast_Cancer_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,11332
+Iris_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,7719
+Iris_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,7718
+Churn_Modelling_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,1843
+adult_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,227
+BankNoteAuthentication_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,9845
+Iris_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,7724
+Wine_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,8229
+Breast_Cancer_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,11331
+BankNoteAuthentication_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,9851
+Breast_Cancer_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,11326
+Iris_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,7716
+Breast_Cancer_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,11325
+BankNoteAuthentication_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,9854
+Breast_Cancer_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,11328
+Iris_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,7720
+diabetes_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,10355
+adult_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,228
+diabetes_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,10354
+Breast_Cancer_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,11324
+vehicle_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,4279
+vehicle_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,4276
+vehicle_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,4282
+vehicle_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,4278
+diabetes_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,10356
+vehicle_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,4284
+adult_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,220
+Churn_Modelling_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,1841
+Wine_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,8230
+Breast_Cancer_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,11329
+BankNoteAuthentication_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,9849
+diabetes_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,10348
+BankNoteAuthentication_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,9847
+Wine_overfitting_knn.png,KNN with 5 neighbour is in overfitting.,8233
+vehicle_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,4283
+vehicle_overfitting_knn.png,KNN with 7 neighbour is in overfitting.,4281
+Wine_overfitting_knn.png,KNN with 10 neighbour is in overfitting.,8238
+Wine_overfitting_knn.png,KNN with 9 neighbour is in overfitting.,8237
+diabetes_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,10350
+Wine_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,8234
+Wine_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,8231
+Wine_overfitting_knn.png,KNN with 4 neighbour is in overfitting.,8232
+diabetes_overfitting_knn.png,KNN with 6 neighbour is in overfitting.,10352
+vehicle_overfitting_knn.png,KNN with 1 neighbour is in overfitting.,4275
+BankNoteAuthentication_overfitting_knn.png,KNN with 2 neighbour is in overfitting.,9846
+Churn_Modelling_overfitting_knn.png,KNN with 3 neighbour is in overfitting.,1839
+adult_overfitting_knn.png,KNN with 8 neighbour is in overfitting.,226
+heart_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,2748
+diabetes_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,10344
+heart_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,2754
+Breast_Cancer_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,11310
+Churn_Modelling_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,1825
+adult_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,217
+adult_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,212
+adult_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,215
+BankNoteAuthentication_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,9842
+Wine_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,8219
+Wine_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,8224
+heart_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,2744
+heart_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,2746
+vehicle_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,4274
+heart_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,2752
+adult_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,214
+diabetes_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,10341
+Wine_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,8217
+vehicle_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,4267
+BankNoteAuthentication_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,9834
+Churn_Modelling_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,1832
+Churn_Modelling_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,1834
+Breast_Cancer_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,11314
+Wine_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,8221
+diabetes_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,10335
+Churn_Modelling_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,1835
+Churn_Modelling_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,1829
+Churn_Modelling_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,1828
+vehicle_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,4264
+vehicle_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,4271
+Wine_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,8215
+adult_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,209
+vehicle_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,4261
+Breast_Cancer_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,11317
+adult_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,218
+Breast_Cancer_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,11315
+adult_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,213
+BankNoteAuthentication_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,9836
+diabetes_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,10336
+BankNoteAuthentication_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,9839
+vehicle_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,4265
+adult_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,205
+Breast_Cancer_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,11313
+Wine_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,8223
+Breast_Cancer_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,11319
+BankNoteAuthentication_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,9838
+Churn_Modelling_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,1836
+Breast_Cancer_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,11320
+Iris_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,7714
+Churn_Modelling_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,1830
+vehicle_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,4262
+adult_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,210
+adult_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,208
+adult_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,207
+vehicle_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,4266
+Iris_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,7709
+Churn_Modelling_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,1823
+Churn_Modelling_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,1833
+Breast_Cancer_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,11321
+BankNoteAuthentication_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,9837
+diabetes_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,10342
+Breast_Cancer_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,11318
+diabetes_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,10340
+Wine_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,8222
+diabetes_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,10334
+adult_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,206
+Iris_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,7707
+Iris_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,7702
+BankNoteAuthentication_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,9841
+vehicle_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,4272
+Wine_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,8216
+Iris_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,7715
+adult_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,216
+Iris_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,7713
+BankNoteAuthentication_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,9844
+Iris_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,7712
+heart_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,2747
+vehicle_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,4269
+Breast_Cancer_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,11312
+Breast_Cancer_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,11322
+Wine_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,8228
+BankNoteAuthentication_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,9843
+Iris_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,7710
+heart_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,2753
+diabetes_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,10345
+BankNoteAuthentication_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,9835
+Churn_Modelling_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,1831
+BankNoteAuthentication_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,9831
+BankNoteAuthentication_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,9832
+adult_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,211
+Breast_Cancer_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,11311
+Iris_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,7703
+BankNoteAuthentication_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,9833
+heart_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,2755
+Wine_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,8225
+Churn_Modelling_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,1826
+Iris_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,7704
+Wine_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,8218
+diabetes_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,10337
+Iris_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,7706
+heart_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,2756
+heart_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,2750
+Iris_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,7711
+diabetes_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,10339
+diabetes_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,10333
+Churn_Modelling_overfitting_knn.png,KNN with less than 2 neighbours is in overfitting.,1824
+Churn_Modelling_overfitting_knn.png,KNN with more than 4 neighbours is in overfitting.,1827
+vehicle_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,4263
+Wine_overfitting_knn.png,KNN with less than 7 neighbours is in overfitting.,8226
+heart_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,2749
+BankNoteAuthentication_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,9840
+diabetes_overfitting_knn.png,KNN with less than 8 neighbours is in overfitting.,10346
+diabetes_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,10338
+Iris_overfitting_knn.png,KNN with more than 5 neighbours is in overfitting.,7708
+heart_overfitting_knn.png,KNN with more than 3 neighbours is in overfitting.,2745
+Breast_Cancer_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,11309
+diabetes_overfitting_knn.png,KNN with more than 7 neighbours is in overfitting.,10343
+Breast_Cancer_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,11316
+heart_overfitting_knn.png,KNN with more than 2 neighbours is in overfitting.,2743
+vehicle_overfitting_knn.png,KNN with less than 5 neighbours is in overfitting.,4268
+Wine_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,8227
+vehicle_overfitting_knn.png,KNN with more than 8 neighbours is in overfitting.,4273
+heart_overfitting_knn.png,KNN with more than 6 neighbours is in overfitting.,2751
+vehicle_overfitting_knn.png,KNN with less than 6 neighbours is in overfitting.,4270
+Iris_overfitting_knn.png,KNN with less than 3 neighbours is in overfitting.,7705
+Wine_overfitting_knn.png,KNN with less than 4 neighbours is in overfitting.,8220
+Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",1819
+Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",1822
+Iris_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",7697
+heart_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",2738
+Iris_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",7698
+vehicle_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",4255
+Iris_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",7701
+Wine_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",8211
+BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",9828
+BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",9825
+Iris_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",7696
+adult_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",204
+vehicle_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",4257
+heart_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",2737
+vehicle_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",4258
+BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",9826
+diabetes_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",10330
+heart_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",2740
+Iris_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",7699
+diabetes_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",10331
+Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",11305
+BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",9830
+adult_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",203
+heart_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",2739
+Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",1817
+Wine_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",8210
+BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",9827
+vehicle_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",4259
+Wine_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",8212
+adult_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",202
+Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",1818
+Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",11307
+Iris_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",7700
+adult_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",199
+Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",11308
+Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",11303
+Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",1820
+diabetes_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",10327
+vehicle_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",4256
+adult_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",200
+Wine_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in underfitting.",8209
+Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 2, may be explained by its estimators being in overfitting.",11306
+heart_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",2742
+diabetes_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",10332
+vehicle_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",4260
+Wine_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",8213
+Wine_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in overfitting.",8214
+adult_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",201
+heart_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",2741
+Breast_Cancer_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",11304
+Churn_Modelling_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",1821
+diabetes_overfitting_rf.png,"Results for Random Forests identified as 10, may be explained by its estimators being in underfitting.",10329
+diabetes_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in underfitting.",10328
+BankNoteAuthentication_overfitting_rf.png,"Results for Random Forests identified as 3, may be explained by its estimators being in overfitting.",9829
+Wine_decision_tree.png,The recall for the presented tree is higher than 66%.,8186
+Iris_decision_tree.png,The recall for the presented tree is lower than 67%.,7669
+adult_decision_tree.png,The recall for the presented tree is lower than 68%.,180
+heart_decision_tree.png,The recall for the presented tree is higher than 74%.,2698
+heart_decision_tree.png,The precision for the presented tree is higher than 90%.,2715
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 71%.,9823
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 84%.,9794
+heart_decision_tree.png,The specificity for the presented tree is higher than 79%.,2724
+vehicle_decision_tree.png,The accuracy for the presented tree is higher than 90%.,4223
+heart_decision_tree.png,The specificity for the presented tree is lower than 70%.,2704
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 89%.,11295
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 77%.,11282
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than 78%.,10299
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 80%.,11266
+heart_decision_tree.png,The recall for the presented tree is lower than 82%.,2710
+vehicle_decision_tree.png,The specificity for the presented tree is higher than 77%.,4250
+heart_decision_tree.png,The specificity for the presented tree is lower than 63%.,2712
+Wine_decision_tree.png,The specificity for the presented tree is higher than 71%.,8172
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 61%.,1800
+Iris_decision_tree.png,The recall for the presented tree is lower than 60%.,7685
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 82%.,11302
+diabetes_decision_tree.png,The precision for the presented tree is lower than 87%.,10317
+Wine_decision_tree.png,The specificity for the presented tree is lower than 65%.,8176
+adult_decision_tree.png,The precision for the presented tree is higher than 77%.,185
+heart_decision_tree.png,The recall for the presented tree is lower than 86%.,2702
+adult_decision_tree.png,The accuracy for the presented tree is higher than 71%.,167
+Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 77%.,1786
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 90%.,9789
+heart_decision_tree.png,The precision for the presented tree is higher than 60%.,2699
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 65%.,11274
+Iris_decision_tree.png,The specificity for the presented tree is higher than 86%.,7667
+Iris_decision_tree.png,The specificity for the presented tree is higher than 89%.,7675
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than 72%.,10307
+vehicle_decision_tree.png,The accuracy for the presented tree is higher than 84%.,4239
+Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 69%.,11264
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 61%.,9799
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 72%.,9802
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 60%.,9820
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 74%.,9815
+Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 86%.,1798
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 62%.,1789
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 66%.,11290
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 73%.,11263
+diabetes_decision_tree.png,The precision for the presented tree is lower than 60%.,10309
+adult_decision_tree.png,The specificity for the presented tree is lower than 62%.,166
+Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 63%.,11269
+Iris_decision_tree.png,The recall for the presented tree is higher than 85%.,7673
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 87%.,11287
+diabetes_decision_tree.png,The specificity for the presented tree is lower than 87%.,10326
+Iris_decision_tree.png,The recall for the presented tree is lower than 79%.,7661
+heart_decision_tree.png,The precision for the presented tree is higher than 80%.,2723
+Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 71%.,11288
+adult_decision_tree.png,The precision for the presented tree is lower than 61%.,173
+diabetes_decision_tree.png,The accuracy for the presented tree is higher than 81%.,10303
+Wine_decision_tree.png,The precision for the presented tree is lower than 89%.,8191
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 62%.,1813
+Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 79%.,1807
+Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than 90%.,1787
+diabetes_decision_tree.png,The recall for the presented tree is lower than 89%.,10292
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 86%.,11270
+Iris_decision_tree.png,The precision for the presented tree is lower than 80%.,7662
+Wine_decision_tree.png,The accuracy for the presented tree is higher than 72%.,8177
+Iris_decision_tree.png,The specificity for the presented tree is higher than 75%.,7683
+heart_decision_tree.png,The precision for the presented tree is lower than 89%.,2719
+Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 80%.,11301
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 88%.,9814
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 77%.,9786
+heart_decision_tree.png,The specificity for the presented tree is lower than 64%.,2736
+Iris_decision_tree.png,The recall for the presented tree is lower than 87%.,7693
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 68%.,1788
+Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 82%.,1782
+heart_decision_tree.png,The accuracy for the presented tree is lower than 78%.,2725
+diabetes_decision_tree.png,The specificity for the presented tree is higher than 88%.,10314
+Iris_decision_tree.png,The precision for the presented tree is lower than 83%.,7670
+diabetes_decision_tree.png,The recall for the presented tree is higher than 83%.,10288
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 80%.,9811
+diabetes_decision_tree.png,The recall for the presented tree is lower than 71%.,10300
+diabetes_decision_tree.png,The recall for the presented tree is lower than 82%.,10308
+vehicle_decision_tree.png,The specificity for the presented tree is lower than 85%.,4246
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 85%.,11279
+Iris_decision_tree.png,The accuracy for the presented tree is higher than 70%.,7672
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 64%.,9807
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 60%.,11289
+Wine_decision_tree.png,The precision for the presented tree is higher than 83%.,8179
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 60%.,1781
+Iris_decision_tree.png,The precision for the presented tree is lower than 71%.,7686
+Wine_decision_tree.png,The precision for the presented tree is higher than 90%.,8195
+heart_decision_tree.png,The recall for the presented tree is lower than 90%.,2734
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 73%.,9809
+adult_decision_tree.png,The recall for the presented tree is lower than 68%.,196
+Wine_decision_tree.png,The specificity for the presented tree is lower than 80%.,8192
+Wine_decision_tree.png,The recall for the presented tree is lower than 86%.,8174
+adult_decision_tree.png,The specificity for the presented tree is lower than 61%.,190
+vehicle_decision_tree.png,The accuracy for the presented tree is lower than 81%.,4227
+heart_decision_tree.png,The accuracy for the presented tree is higher than 76%.,2697
+adult_decision_tree.png,The precision for the presented tree is lower than 73%.,197
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 88%.,11294
+Iris_decision_tree.png,The specificity for the presented tree is lower than 87%.,7687
+Iris_decision_tree.png,The recall for the presented tree is higher than 65%.,7689
+diabetes_decision_tree.png,The specificity for the presented tree is higher than 85%.,10290
+adult_decision_tree.png,The accuracy for the presented tree is higher than 63%.,159
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 82%.,11300
+Wine_decision_tree.png,The accuracy for the presented tree is higher than 85%.,8201
+Iris_decision_tree.png,The specificity for the presented tree is lower than 64%.,7663
+adult_decision_tree.png,The accuracy for the presented tree is higher than 65%.,175
+adult_decision_tree.png,The precision for the presented tree is higher than 73%.,161
+vehicle_decision_tree.png,The recall for the presented tree is lower than 69%.,4252
+heart_decision_tree.png,The accuracy for the presented tree is higher than 81%.,2713
+Wine_decision_tree.png,The accuracy for the presented tree is lower than 76%.,8173
+Wine_decision_tree.png,The precision for the presented tree is lower than 62%.,8175
+adult_decision_tree.png,The precision for the presented tree is lower than 69%.,181
+vehicle_decision_tree.png,The accuracy for the presented tree is lower than 61%.,4243
+adult_decision_tree.png,The recall for the presented tree is lower than 79%.,164
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 83%.,1812
+diabetes_decision_tree.png,The accuracy for the presented tree is higher than 70%.,10287
+Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 61%.,11272
+vehicle_decision_tree.png,The specificity for the presented tree is higher than 77%.,4218
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than 74%.,10315
+Wine_decision_tree.png,The accuracy for the presented tree is higher than 78%.,8169
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than 65%.,11298
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 81%.,11292
+heart_decision_tree.png,The precision for the presented tree is higher than 83%.,2707
+adult_decision_tree.png,The specificity for the presented tree is lower than 78%.,182
+Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than 76%.,1790
+diabetes_decision_tree.png,The specificity for the presented tree is higher than 65%.,10306
+Wine_decision_tree.png,The precision for the presented tree is higher than 79%.,8187
+Wine_decision_tree.png,The recall for the presented tree is lower than 82%.,8198
+heart_decision_tree.png,The accuracy for the presented tree is higher than 76%.,2729
+heart_decision_tree.png,The specificity for the presented tree is lower than 83%.,2728
+vehicle_decision_tree.png,The accuracy for the presented tree is lower than 63%.,4251
+Iris_decision_tree.png,The precision for the presented tree is higher than 88%.,7682
+Wine_decision_tree.png,The precision for the presented tree is higher than 82%.,8203
+adult_decision_tree.png,The recall for the presented tree is higher than 62%.,192
+heart_decision_tree.png,The accuracy for the presented tree is lower than 85%.,2717
+heart_decision_tree.png,The precision for the presented tree is higher than 86%.,2731
+vehicle_decision_tree.png,The precision for the presented tree is higher than 71%.,4241
+heart_decision_tree.png,The specificity for the presented tree is lower than 69%.,2720
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 87%.,9818
+vehicle_decision_tree.png,The recall for the presented tree is lower than 65%.,4244
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than 75%.,10323
+vehicle_decision_tree.png,The precision for the presented tree is lower than 87%.,4221
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 74%.,9824
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 85%.,1808
+Iris_decision_tree.png,The precision for the presented tree is lower than 69%.,7678
+Iris_decision_tree.png,The accuracy for the presented tree is lower than 84%.,7676
+vehicle_decision_tree.png,The precision for the presented tree is lower than 83%.,4237
+Wine_decision_tree.png,The specificity for the presented tree is lower than 69%.,8208
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 67%.,9801
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 89%.,1784
+Iris_decision_tree.png,The recall for the presented tree is higher than 61%.,7681
+Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 87%.,1802
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 71%.,9817
+diabetes_decision_tree.png,The accuracy for the presented tree is higher than 75%.,10311
+Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than 63%.,1778
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 65%.,9795
+diabetes_decision_tree.png,The specificity for the presented tree is higher than 68%.,10322
+vehicle_decision_tree.png,The precision for the presented tree is higher than 77%.,4249
+vehicle_decision_tree.png,The precision for the presented tree is lower than 87%.,4253
+adult_decision_tree.png,The specificity for the presented tree is higher than 89%.,170
+adult_decision_tree.png,The accuracy for the presented tree is higher than 87%.,191
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 81%.,9800
+diabetes_decision_tree.png,The recall for the presented tree is higher than 79%.,10312
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 64%.,1797
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 66%.,9812
+heart_decision_tree.png,The recall for the presented tree is lower than 68%.,2718
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 63%.,9785
+vehicle_decision_tree.png,The specificity for the presented tree is lower than 69%.,4238
+Iris_decision_tree.png,The precision for the presented tree is higher than 80%.,7690
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than 70%.,1792
+Wine_decision_tree.png,The specificity for the presented tree is higher than 69%.,8180
+heart_decision_tree.png,The precision for the presented tree is lower than 73%.,2703
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 88%.,1777
+vehicle_decision_tree.png,The recall for the presented tree is higher than 79%.,4240
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 79%.,11281
+diabetes_decision_tree.png,The specificity for the presented tree is lower than 62%.,10294
+adult_decision_tree.png,The specificity for the presented tree is lower than 84%.,198
+heart_decision_tree.png,The recall for the presented tree is lower than 65%.,2726
+Iris_decision_tree.png,The specificity for the presented tree is lower than 72%.,7679
+Iris_decision_tree.png,The precision for the presented tree is lower than 88%.,7694
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 78%.,11276
+Wine_decision_tree.png,The accuracy for the presented tree is lower than 74%.,8181
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 71%.,11297
+heart_decision_tree.png,The specificity for the presented tree is higher than 72%.,2716
+Wine_decision_tree.png,The specificity for the presented tree is higher than 68%.,8188
+diabetes_decision_tree.png,The precision for the presented tree is lower than 64%.,10293
+adult_decision_tree.png,The recall for the presented tree is lower than 76%.,172
+Wine_decision_tree.png,The specificity for the presented tree is lower than 75%.,8200
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 72%.,1801
+heart_decision_tree.png,The specificity for the presented tree is higher than 64%.,2708
+Wine_decision_tree.png,The precision for the presented tree is lower than 87%.,8183
+Wine_decision_tree.png,The accuracy for the presented tree is lower than 84%.,8197
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 67%.,11291
+diabetes_decision_tree.png,The precision for the presented tree is higher than 90%.,10297
+Iris_decision_tree.png,The precision for the presented tree is higher than 87%.,7666
+adult_decision_tree.png,The specificity for the presented tree is higher than 87%.,162
+Iris_decision_tree.png,The precision for the presented tree is higher than 76%.,7658
+Wine_decision_tree.png,The specificity for the presented tree is higher than 75%.,8196
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than 68%.,9810
+vehicle_decision_tree.png,The precision for the presented tree is lower than 62%.,4245
+Wine_decision_tree.png,The accuracy for the presented tree is lower than 64%.,8189
+Iris_decision_tree.png,The accuracy for the presented tree is lower than 77%.,7684
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 86%.,9808
+Wine_decision_tree.png,The precision for the presented tree is higher than 73%.,8171
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 74%.,9821
+Iris_decision_tree.png,The specificity for the presented tree is lower than 62%.,7695
+Wine_decision_tree.png,The recall for the presented tree is higher than 84%.,8202
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 84%.,11275
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 77%.,9822
+vehicle_decision_tree.png,The recall for the presented tree is higher than 85%.,4216
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 67%.,1804
+vehicle_decision_tree.png,The precision for the presented tree is higher than 72%.,4225
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 75%.,9787
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 73%.,9819
+Iris_decision_tree.png,The specificity for the presented tree is higher than 68%.,7659
+heart_decision_tree.png,The specificity for the presented tree is higher than 77%.,2732
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 76%.,9813
+vehicle_decision_tree.png,The accuracy for the presented tree is higher than 64%.,4231
+vehicle_decision_tree.png,The specificity for the presented tree is lower than 88%.,4222
+diabetes_decision_tree.png,The recall for the presented tree is lower than 73%.,10324
+vehicle_decision_tree.png,The specificity for the presented tree is higher than 82%.,4242
+Iris_decision_tree.png,The accuracy for the presented tree is lower than 73%.,7660
+Iris_decision_tree.png,The accuracy for the presented tree is higher than 90%.,7680
+vehicle_decision_tree.png,The recall for the presented tree is lower than 70%.,4236
+diabetes_decision_tree.png,The recall for the presented tree is higher than 86%.,10304
+diabetes_decision_tree.png,The specificity for the presented tree is lower than 63%.,10310
+adult_decision_tree.png,The accuracy for the presented tree is higher than 88%.,183
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 60%.,9797
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 80%.,1785
+diabetes_decision_tree.png,The specificity for the presented tree is higher than 68%.,10298
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 68%.,11283
+Wine_decision_tree.png,The precision for the presented tree is lower than 87%.,8207
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 89%.,11273
+heart_decision_tree.png,The precision for the presented tree is lower than 84%.,2711
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than 64%.,9816
+Iris_decision_tree.png,The specificity for the presented tree is higher than 65%.,7691
+Wine_decision_tree.png,The recall for the presented tree is lower than 81%.,8182
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than 82%.,9793
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 70%.,11284
+diabetes_decision_tree.png,The precision for the presented tree is higher than 76%.,10313
+Iris_decision_tree.png,The recall for the presented tree is lower than 66%.,7677
+diabetes_decision_tree.png,The precision for the presented tree is higher than 70%.,10321
+Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 84%.,1799
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 81%.,1793
+Iris_decision_tree.png,The accuracy for the presented tree is lower than 85%.,7692
+diabetes_decision_tree.png,The precision for the presented tree is higher than 77%.,10305
+adult_decision_tree.png,The recall for the presented tree is higher than 80%.,176
+vehicle_decision_tree.png,The specificity for the presented tree is lower than 85%.,4254
+vehicle_decision_tree.png,The accuracy for the presented tree is higher than 80%.,4215
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than 88%.,11278
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than 61%.,10291
+heart_decision_tree.png,The accuracy for the presented tree is lower than 75%.,2701
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 85%.,9806
+vehicle_decision_tree.png,The specificity for the presented tree is higher than 60%.,4226
+diabetes_decision_tree.png,The precision for the presented tree is lower than 66%.,10301
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than 76%.,11268
+Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than 78%.,1783
+diabetes_decision_tree.png,The specificity for the presented tree is lower than 80%.,10302
+Wine_decision_tree.png,The recall for the presented tree is lower than 88%.,8190
+vehicle_decision_tree.png,The recall for the presented tree is higher than 76%.,4224
+vehicle_decision_tree.png,The precision for the presented tree is higher than 67%.,4217
+adult_decision_tree.png,The accuracy for the presented tree is lower than 67%.,179
+adult_decision_tree.png,The accuracy for the presented tree is lower than 81%.,187
+Iris_decision_tree.png,The accuracy for the presented tree is lower than 78%.,7668
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than 75%.,1796
+Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than 83%.,11277
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than 83%.,1805
+Wine_decision_tree.png,The specificity for the presented tree is higher than 62%.,8204
+vehicle_decision_tree.png,The precision for the presented tree is higher than 63%.,4233
+Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than 77%.,11296
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than 62%.,9791
+heart_decision_tree.png,The accuracy for the presented tree is lower than 70%.,2733
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than 90%.,11271
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than 72%.,11265
+diabetes_decision_tree.png,The recall for the presented tree is higher than 69%.,10296
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 70%.,9788
+heart_decision_tree.png,The accuracy for the presented tree is higher than 88%.,2705
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than 83%.,9805
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than 69%.,9798
+heart_decision_tree.png,The accuracy for the presented tree is lower than 67%.,2709
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than 75%.,11267
+Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than 74%.,1795
+Iris_decision_tree.png,The specificity for the presented tree is lower than 81%.,7671
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than 60%.,1809
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than 78%.,9803
+adult_decision_tree.png,The recall for the presented tree is lower than 82%.,188
+adult_decision_tree.png,The accuracy for the presented tree is lower than 64%.,163
+Iris_decision_tree.png,The recall for the presented tree is higher than 74%.,7665
+Wine_decision_tree.png,The specificity for the presented tree is lower than 67%.,8184
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than 87%.,9796
+Iris_decision_tree.png,The precision for the presented tree is higher than 82%.,7674
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,4214
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,1770
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,8165
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,1771
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,9781
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,1774
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,11256
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,10284
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,156
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,10283
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,11260
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,155
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,2695
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,4213
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,4209
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,7654
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,9780
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,11255
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,9782
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,7655
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,7651
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,10286
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,11262
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,1769
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,9777
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,4212
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,153
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,9779
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,10285
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,158
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,7652
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,8162
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,4207
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,2694
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,7653
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,8168
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,152
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,1773
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,1775
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 8.,8166
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,8163
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,11261
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,2690
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,154
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,8164
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,2696
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,4208
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,11259
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,10279
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,11257
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,2692
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,10280
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,157
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,8167
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,7648
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,1776
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,4211
+adult_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,151
+Churn_Modelling_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,1772
+Breast_Cancer_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,11258
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,10282
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 9.,9783
+diabetes_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,10281
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,7649
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 4.,9778
+Iris_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,7650
+BankNoteAuthentication_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 10.,9784
+Wine_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,8161
+vehicle_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 6.,4210
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 5.,2691
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 7.,2693
+heart_overfitting_decision_tree.png,The chart reporting the recall for different trees shows that the model enters in overfitting for models with depth higher than 3.,2689
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,2682
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,9776
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,2684
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,4202
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,8157
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,9771
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,7640
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,8154
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,10272
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,11253
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,8160
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,7645
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,11252
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,9773
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,1763
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,147
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,149
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,4199
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,1764
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,4203
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,1765
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,1761
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,2688
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,4206
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,11254
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,2685
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,11249
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,4204
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,2683
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,2681
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,9774
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,7646
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,7643
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,10271
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,150
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,8156
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,1766
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,7644
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,11248
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,10273
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,1762
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,148
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,9775
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,9772
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,4201
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,10278
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,10277
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,10275
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,8153
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,146
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,145
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,144
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,2686
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,4200
+adult_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,143
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 7.,11251
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,7647
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,9769
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,7642
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,11250
+BankNoteAuthentication_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,9770
+heart_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,2687
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,1767
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,8159
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,8158
+vehicle_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 9.,4205
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 8.,10276
+Iris_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 4.,7641
+Breast_Cancer_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 3.,11247
+Wine_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 5.,8155
+diabetes_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 6.,10274
+Churn_Modelling_overfitting_decision_tree.png,The decision tree is in overfitting for depths above 10.,1768
+BankNoteAuthentication_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,9739
+adult_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,104
+Breast_Cancer_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,11214
+heart_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,2654
+Churn_Modelling_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,1719
+diabetes_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,10243
+Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 39.,1756
+Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 14.,1755
+BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 48.,9748
+Breast_Cancer_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,11211
+Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 32.,1746
+Iris_decision_tree.png,The number of True Negatives reported in the same tree is 41.,7622
+Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 15.,11239
+BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 19.,9764
+Wine_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,8113
+BankNoteAuthentication_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,9731
+Iris_decision_tree.png,The number of False Positives reported in the same tree is 14.,7625
+Iris_decision_tree.png,The number of False Positives reported in the same tree is 10.,7629
+Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 35.,1751
+heart_decision_tree.png,The number of False Negatives reported in the same tree is 17.,2679
+BankNoteAuthentication_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,9726
+Breast_Cancer_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,11216
+BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 28.,9756
+Churn_Modelling_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,1735
+Churn_Modelling_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,1733
+adult_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,107
+Wine_decision_tree.png,The number of False Positives reported in the same tree is 43.,8150
+Iris_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,7615
+Churn_Modelling_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,1720
+heart_decision_tree.png,The number of True Negatives reported in the same tree is 19.,2662
+adult_decision_tree.png,The number of True Negatives reported in the same tree is 22.,136
+vehicle_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,4160
+heart_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,2649
+diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 17.,10269
+vehicle_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,4155
+Churn_Modelling_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,1721
+Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 33.,11235
+BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 39.,9753
+Wine_decision_tree.png,The number of False Positives reported in the same tree is 45.,8134
+diabetes_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,10228
+diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 26.,10256
+vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 26.,4197
+Iris_decision_tree.png,The number of False Negatives reported in the same tree is 29.,7623
+Iris_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,7597
+vehicle_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,4158
+Wine_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,8130
+diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 10.,10252
+heart_decision_tree.png,The number of False Negatives reported in the same tree is 47.,2667
+Wine_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,8120
+Churn_Modelling_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,1722
+diabetes_decision_tree.png,The number of True Positives reported in the same tree is 33.,10262
+Iris_decision_tree.png,The number of False Positives reported in the same tree is 17.,7621
+Breast_Cancer_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,11204
+Wine_decision_tree.png,The number of False Negatives reported in the same tree is 47.,8152
+Iris_decision_tree.png,The number of False Negatives reported in the same tree is 49.,7627
+BankNoteAuthentication_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,9741
+Wine_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,8115
+heart_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,2636
+Iris_decision_tree.png,The number of False Negatives reported in the same tree is 24.,7635
+Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 40.,1759
+Churn_Modelling_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,1731
+vehicle_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,4177
+Wine_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,8128
+heart_decision_tree.png,The number of True Positives reported in the same tree is 29.,2672
+Churn_Modelling_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,1723
+Churn_Modelling_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,1716
+Wine_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,8125
+heart_decision_tree.png,The number of False Positives reported in the same tree is 44.,2677
+Wine_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,8112
+adult_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,115
+vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 32.,4198
+Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 15.,1752
+Iris_decision_tree.png,The number of False Positives reported in the same tree is 15.,7633
+Churn_Modelling_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,1732
+BankNoteAuthentication_decision_tree.png,The number of False Negatives reported in the same tree is 35.,9763
+BankNoteAuthentication_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,9740
+Iris_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,7613
+BankNoteAuthentication_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,9727
+adult_decision_tree.png,The number of False Negatives reported in the same tree is 30.,137
+Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 46.,11229
+Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 16.,11234
+Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 17.,1754
+BankNoteAuthentication_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,9738
+diabetes_decision_tree.png,The number of False Positives reported in the same tree is 23.,10259
+Churn_Modelling_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,1717
+Iris_decision_tree.png,The number of True Negatives reported in the same tree is 42.,7638
+Breast_Cancer_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,11202
+BankNoteAuthentication_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,9725
+BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 20.,9754
+Wine_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,8127
+Iris_decision_tree.png,The number of True Positives reported in the same tree is 43.,7628
+Wine_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,8126
+Wine_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,8116
+adult_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,100
+Iris_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,7610
+adult_decision_tree.png,The number of True Positives reported in the same tree is 23.,138
+adult_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,103
+Breast_Cancer_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,11218
+Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 20.,11230
+Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 19.,11227
+Wine_decision_tree.png,The number of True Negatives reported in the same tree is 38.,8143
+BankNoteAuthentication_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,9728
+Wine_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,8123
+Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 11.,1750
+Wine_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,8110
+heart_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,2644
+Wine_decision_tree.png,The number of False Negatives reported in the same tree is 46.,8148
+Churn_Modelling_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,1729
+Breast_Cancer_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,11224
+vehicle_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,4175
+heart_decision_tree.png,The number of False Negatives reported in the same tree is 32.,2675
+vehicle_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,4156
+Iris_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,7606
+Wine_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,8111
+BankNoteAuthentication_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,9745
+BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 29.,9749
+Breast_Cancer_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,11208
+Iris_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,7602
+Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 13.,11243
+diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 24.,10268
+Iris_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,7616
+vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 28.,4194
+Breast_Cancer_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,11220
+diabetes_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,10235
+adult_decision_tree.png,The number of False Positives reported in the same tree is 35.,123
+heart_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,2638
+heart_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,2641
+Breast_Cancer_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,11207
+Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 38.,11226
+Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 14.,11233
+Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 27.,1744
+vehicle_decision_tree.png,The number of True Positives reported in the same tree is 43.,4191
+BankNoteAuthentication_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,9735
+Iris_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,7598
+Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 37.,11244
+Wine_decision_tree.png,The number of False Positives reported in the same tree is 42.,8142
+diabetes_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,10234
+diabetes_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,10232
+diabetes_decision_tree.png,The number of False Positives reported in the same tree is 48.,10251
+BankNoteAuthentication_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,9737
+adult_decision_tree.png,The number of True Positives reported in the same tree is 17.,126
+diabetes_decision_tree.png,The number of False Positives reported in the same tree is 41.,10263
+Iris_decision_tree.png,The number of True Positives reported in the same tree is 27.,7620
+BankNoteAuthentication_decision_tree.png,The number of False Positives is higher than the number of True Positives for the presented tree.,9724
+Wine_decision_tree.png,The number of True Positives reported in the same tree is 34.,8149
+Iris_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,7612
+Breast_Cancer_decision_tree.png,The number of True Positives reported in the same tree is 10.,11242
+heart_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,2637
+adult_decision_tree.png,The number of False Positives reported in the same tree is 50.,127
+adult_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,112
+adult_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,102
+vehicle_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,4157
+Wine_decision_tree.png,The number of True Positives reported in the same tree is 23.,8137
+Breast_Cancer_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,11203
+Churn_Modelling_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,1726
+Breast_Cancer_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,11213
+BankNoteAuthentication_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,9736
+heart_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,2652
+Churn_Modelling_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,1736
+heart_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,2647
+diabetes_decision_tree.png,The number of True Negatives reported in the same tree is 15.,10260
+heart_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,2639
+adult_decision_tree.png,The number of True Positives reported in the same tree is 25.,130
+diabetes_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,10231
+Iris_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,7614
+heart_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,2648
+Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 41.,1745
+Wine_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,8132
+Iris_decision_tree.png,The number of True Negatives reported in the same tree is 37.,7630
+BankNoteAuthentication_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,9730
+Breast_Cancer_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,11223
+heart_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,2658
+Wine_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,8122
+Breast_Cancer_decision_tree.png,The number of False Positives reported in the same tree is 17.,11231
+Iris_decision_tree.png,The number of True Negatives reported in the same tree is 46.,7634
+Wine_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,8131
+BankNoteAuthentication_decision_tree.png,The number of False Negatives reported in the same tree is 27.,9755
+diabetes_decision_tree.png,The number of True Positives is higher than the number of False Negatives for the presented tree.,10244
+Wine_decision_tree.png,The number of True Positives reported in the same tree is 50.,8141
+adult_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,118
+Iris_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,7607
+heart_decision_tree.png,The number of True Negatives reported in the same tree is 26.,2678
+BankNoteAuthentication_decision_tree.png,The number of True Positives reported in the same tree is 43.,9760
+Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 23.,11232
+vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 30.,4185
+diabetes_decision_tree.png,The number of False Positives is higher than the number of True Negatives for the presented tree.,10239
+Iris_decision_tree.png,The number of False Positives reported in the same tree is 26.,7637
+Churn_Modelling_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,1727
+vehicle_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,4176
+diabetes_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,10248
+heart_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,2640
+diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 39.,10253
+adult_decision_tree.png,The number of False Positives reported in the same tree is 21.,139
+Wine_decision_tree.png,The number of False Negatives reported in the same tree is 36.,8140
+diabetes_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,10233
+heart_decision_tree.png,The number of False Negatives reported in the same tree is 35.,2671
+vehicle_decision_tree.png,The number of False Negatives is lower than the number of True Negatives for the presented tree.,4172
+heart_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,2657
+Churn_Modelling_decision_tree.png,The number of False Negatives reported in the same tree is 33.,1747
+BankNoteAuthentication_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,9746
+BankNoteAuthentication_decision_tree.png,The number of False Positives reported in the same tree is 15.,9765
+BankNoteAuthentication_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,9729
+adult_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,101
+heart_decision_tree.png,The number of True Negatives reported in the same tree is 23.,2674
+diabetes_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,10246
+Breast_Cancer_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,11209
+vehicle_decision_tree.png,The number of True Positives reported in the same tree is 44.,4183
+Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 13.,1749
+Iris_decision_tree.png,The number of True Positives reported in the same tree is 47.,7632
+adult_decision_tree.png,The number of True Negatives reported in the same tree is 28.,132
+adult_decision_tree.png,The number of False Negatives reported in the same tree is 33.,133
+Breast_Cancer_decision_tree.png,The number of False Negatives reported in the same tree is 31.,11245
+diabetes_decision_tree.png,The number of True Positives is lower than the number of False Negatives for the presented tree.,10247
+Breast_Cancer_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,11205
+adult_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,114
+adult_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,105
+vehicle_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,4166
+BankNoteAuthentication_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,9732
+Churn_Modelling_decision_tree.png,The number of False Negatives is higher than the number of True Positives for the presented tree.,1718
+Iris_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,7603
+vehicle_decision_tree.png,The number of False Positives reported in the same tree is 35.,4196
+heart_decision_tree.png,The number of True Negatives is higher than the number of False Negatives for the presented tree.,2656
+adult_decision_tree.png,The number of False Negatives reported in the same tree is 41.,129
+Wine_decision_tree.png,The number of True Negatives reported in the same tree is 27.,8151
+diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 42.,10261
+diabetes_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,10240
+Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 34.,11240
+vehicle_decision_tree.png,The number of False Negatives is higher than the number of False Positives for the presented tree.,4163
+Breast_Cancer_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,11217
+Wine_decision_tree.png,The number of False Positives reported in the same tree is 13.,8138
+Iris_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,7618
+BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 38.,9758
+adult_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,113
+adult_decision_tree.png,The number of True Negatives is lower than the number of False Positives for the presented tree.,108
+diabetes_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,10249
+vehicle_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,4174
+Wine_decision_tree.png,The number of False Negatives reported in the same tree is 39.,8144
+Iris_decision_tree.png,The number of False Positives is lower than the number of True Positives for the presented tree.,7599
+vehicle_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,4161
+adult_decision_tree.png,The number of False Positives is lower than the number of False Negatives for the presented tree.,120
+adult_decision_tree.png,The number of True Positives reported in the same tree is 12.,122
+vehicle_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,4167
+diabetes_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,10238
+vehicle_decision_tree.png,The number of False Positives is lower than the number of True Negatives for the presented tree.,4171
+vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 50.,4189
+Breast_Cancer_decision_tree.png,The number of True Negatives reported in the same tree is 24.,11228
+Wine_decision_tree.png,The number of False Negatives is lower than the number of True Positives for the presented tree.,8114
+adult_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,110
+vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 38.,4186
+vehicle_decision_tree.png,The number of False Negatives is higher than the number of True Negatives for the presented tree.,4169
+Wine_decision_tree.png,The number of False Negatives reported in the same tree is 29.,8136
+diabetes_decision_tree.png,The number of True Negatives is higher than the number of True Positives for the presented tree.,10227
+BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 45.,9750
+Churn_Modelling_decision_tree.png,The number of False Positives reported in the same tree is 10.,1757
+vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 21.,4181
+BankNoteAuthentication_decision_tree.png,The number of True Negatives reported in the same tree is 33.,9762
+Churn_Modelling_decision_tree.png,The number of True Positives reported in the same tree is 29.,1740
+Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 43.,1758
+heart_decision_tree.png,The number of True Positives is higher than the number of False Positives for the presented tree.,2642
+Iris_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,7605
+heart_decision_tree.png,The number of False Positives reported in the same tree is 38.,2661
+heart_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,2651
+adult_decision_tree.png,The number of True Negatives reported in the same tree is 37.,124
+BankNoteAuthentication_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,9733
+vehicle_decision_tree.png,The number of True Negatives reported in the same tree is 33.,4193
+heart_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,2645
+diabetes_decision_tree.png,The number of False Positives reported in the same tree is 30.,10267
+diabetes_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,10241
+diabetes_decision_tree.png,The number of True Negatives is lower than the number of True Positives for the presented tree.,10230
+vehicle_decision_tree.png,The number of True Negatives is higher than the number of False Positives for the presented tree.,4162
+diabetes_decision_tree.png,The number of False Negatives reported in the same tree is 28.,10265
+Wine_decision_tree.png,The number of True Negatives reported in the same tree is 44.,8135
+Churn_Modelling_decision_tree.png,The number of True Positives is higher than the number of True Negatives for the presented tree.,1728
+heart_decision_tree.png,The number of True Negatives is lower than the number of False Negatives for the presented tree.,2659
+vehicle_decision_tree.png,The number of True Positives reported in the same tree is 20.,4187
+Iris_decision_tree.png,The number of True Positives is lower than the number of True Negatives for the presented tree.,7611
+heart_decision_tree.png,The number of False Positives reported in the same tree is 40.,2669
+vehicle_decision_tree.png,The number of False Negatives reported in the same tree is 15.,4182
+heart_decision_tree.png,The number of False Positives reported in the same tree is 18.,2665
+Churn_Modelling_decision_tree.png,The number of True Negatives reported in the same tree is 19.,1742
+diabetes_decision_tree.png,The number of True Positives reported in the same tree is 11.,10266
+heart_decision_tree.png,The number of True Positives reported in the same tree is 11.,2668
+Wine_decision_tree.png,The number of True Positives reported in the same tree is 32.,8145
+Churn_Modelling_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,1725
+heart_decision_tree.png,The number of True Positives reported in the same tree is 45.,2676
+Iris_decision_tree.png,The number of True Negatives reported in the same tree is 45.,7626
+diabetes_decision_tree.png,The number of False Negatives is lower than the number of False Positives for the presented tree.,10237
+vehicle_decision_tree.png,The number of True Positives is lower than the number of False Positives for the presented tree.,4164
+diabetes_decision_tree.png,The number of False Positives is higher than the number of False Negatives for the presented tree.,10245
+Iris_decision_tree.png,The number of False Negatives reported in the same tree is 33.,7631
+heart_decision_tree.png,The accuracy for the presented tree is higher than its precision.,2624
+heart_decision_tree.png,The specificity for the presented tree is lower than its recall.,2623
+diabetes_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,10270
+Iris_decision_tree.png,The precision for the presented tree is lower than its specificity.,7595
+diabetes_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,10204
+adult_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,76
+vehicle_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,6991
+vehicle_decision_tree.png,The precision for the presented tree is higher than its accuracy.,4132
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than its specificity.,11198
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than its recall.,11185
+adult_decision_tree.png,The accuracy for the presented tree is higher than its precision.,86
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,11180
+vehicle_decision_tree.png,The precision for the presented tree is lower than its accuracy.,4135
+heart_decision_tree.png,The recall for the presented tree is higher than its precision.,2625
+Iris_decision_tree.png,The recall for the presented tree is higher than its accuracy.,7572
+heart_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,2611
+vehicle_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,4133
+heart_decision_tree.png,The recall for the presented tree is lower than its precision.,2628
+adult_decision_tree.png,The accuracy for the presented tree is lower than its recall.,83
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than its specificity.,9723
+Wine_decision_tree.png,The specificity for the presented tree is higher than its recall.,8093
+Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than its specificity.,1715
+Wine_decision_tree.png,The precision for the presented tree is higher than its recall.,8092
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than its accuracy.,11181
+vehicle_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,4136
+diabetes_decision_tree.png,The precision for the presented tree is higher than its specificity.,10222
+Wine_decision_tree.png,The precision for the presented tree is lower than its specificity.,8108
+diabetes_decision_tree.png,The recall for the presented tree is lower than its accuracy.,10205
+diabetes_decision_tree.png,The precision for the presented tree is higher than its recall.,10209
+heart_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,3830
+diabetes_decision_tree.png,The specificity for the presented tree is lower than its recall.,10213
+vehicle_decision_tree.png,The specificity for the presented tree is lower than its precision.,4148
+Iris_decision_tree.png,The recall for the presented tree is lower than its precision.,7588
+Breast_Cancer_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,12506
+vehicle_decision_tree.png,The recall for the presented tree is lower than its precision.,4147
+vehicle_decision_tree.png,The specificity for the presented tree is lower than its recall.,4142
+Wine_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,8103
+diabetes_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,10969
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than its recall.,11184
+vehicle_decision_tree.png,The accuracy for the presented tree is lower than its recall.,4140
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,1710
+Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than its specificity.,1711
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than its precision.,11194
+heart_decision_tree.png,The recall for the presented tree is lower than its specificity.,2634
+heart_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,2633
+vehicle_decision_tree.png,The recall for the presented tree is higher than its precision.,4144
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than its precision.,11195
+adult_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,73
+heart_decision_tree.png,The accuracy for the presented tree is lower than its recall.,2621
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than its precision.,9714
+adult_decision_tree.png,The accuracy for the presented tree is lower than its precision.,89
+adult_decision_tree.png,The specificity for the presented tree is higher than its recall.,82
+adult_decision_tree.png,The recall for the presented tree is higher than its precision.,87
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than its specificity.,9720
+diabetes_decision_tree.png,The precision for the presented tree is lower than its accuracy.,10206
+heart_decision_tree.png,The specificity for the presented tree is higher than its recall.,2620
+heart_decision_tree.png,The precision for the presented tree is lower than its specificity.,2635
+heart_decision_tree.png,The specificity for the presented tree is higher than its precision.,2626
+Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than its specificity.,11201
+vehicle_decision_tree.png,The precision for the presented tree is higher than its recall.,4138
+Wine_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,9389
+heart_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,2680
+Iris_decision_tree.png,The precision for the presented tree is higher than its specificity.,7592
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,11183
+Iris_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,7936
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,1694
+Iris_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,7577
+Iris_decision_tree.png,The precision for the presented tree is lower than its accuracy.,7576
+Churn_Modelling_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,1760
+adult_decision_tree.png,The precision for the presented tree is lower than its specificity.,97
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than its precision.,11190
+Wine_decision_tree.png,The accuracy for the presented tree is higher than its recall.,8091
+Wine_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,8084
+heart_decision_tree.png,The precision for the presented tree is lower than its recall.,2622
+diabetes_decision_tree.png,The recall for the presented tree is lower than its precision.,10218
+heart_decision_tree.png,The recall for the presented tree is higher than its specificity.,2631
+Iris_decision_tree.png,The precision for the presented tree is higher than its accuracy.,7573
+Iris_decision_tree.png,The accuracy for the presented tree is lower than its recall.,7581
+Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than its accuracy.,11182
+diabetes_decision_tree.png,The precision for the presented tree is lower than its specificity.,10225
+Breast_Cancer_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,11177
+adult_decision_tree.png,The specificity for the presented tree is lower than its precision.,91
+diabetes_decision_tree.png,The accuracy for the presented tree is higher than its recall.,10208
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than its accuracy.,9703
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than its recall.,1700
+diabetes_decision_tree.png,The precision for the presented tree is higher than its accuracy.,10203
+Breast_Cancer_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,11246
+Iris_decision_tree.png,The precision for the presented tree is lower than its recall.,7582
+Wine_decision_tree.png,The accuracy for the presented tree is lower than its recall.,8094
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,9702
+Wine_decision_tree.png,The recall for the presented tree is lower than its accuracy.,8088
+Iris_decision_tree.png,The accuracy for the presented tree is higher than its recall.,7578
+heart_decision_tree.png,The recall for the presented tree is higher than its accuracy.,2612
+BankNoteAuthentication_histograms_numeric.png,The existence of outliers is one of the problems to tackle in this dataset.,10065
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than its accuracy.,9701
+adult_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,79
+heart_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,2630
+Iris_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,7590
+Iris_decision_tree.png,The recall for the presented tree is higher than its specificity.,7591
+vehicle_decision_tree.png,The recall for the presented tree is higher than its accuracy.,4131
+Iris_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,7571
+vehicle_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,4149
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than its precision.,10217
+heart_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,2617
+vehicle_decision_tree.png,The precision for the presented tree is lower than its recall.,4141
+Wine_decision_tree.png,The precision for the presented tree is higher than its accuracy.,8086
+Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than its recall.,1699
+heart_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,2614
+diabetes_decision_tree.png,The specificity for the presented tree is higher than its recall.,10210
+Iris_decision_tree.png,The specificity for the presented tree is lower than its recall.,7583
+diabetes_decision_tree.png,The recall for the presented tree is lower than its specificity.,10224
+Churn_Modelling_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,1691
+adult_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,142
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than its precision.,1704
+Wine_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,8106
+Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than its precision.,1708
+Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than its accuracy.,1692
+adult_decision_tree.png,The accuracy for the presented tree is higher than its recall.,80
+adult_decision_tree.png,The precision for the presented tree is lower than its accuracy.,78
+adult_decision_tree.png,The specificity for the presented tree is higher than its precision.,88
+heart_decision_tree.png,The recall for the presented tree is lower than its accuracy.,2615
+adult_decision_tree.png,The precision for the presented tree is higher than its recall.,81
+adult_decision_tree.png,The precision for the presented tree is lower than its recall.,84
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,1713
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than its recall.,9706
+adult_decision_tree.png,The recall for the presented tree is lower than its accuracy.,77
+Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than its specificity.,11197
+diabetes_decision_tree.png,The specificity for the presented tree is lower than its precision.,10219
+Churn_Modelling_decision_tree.png,The recall for the presented tree is higher than its precision.,1705
+vehicle_decision_tree.png,The specificity for the presented tree is higher than its precision.,4145
+Wine_decision_tree.png,The specificity for the presented tree is lower than its recall.,8096
+heart_decision_tree.png,The accuracy for the presented tree is lower than its precision.,2627
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than its recall.,1703
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than its precision.,1709
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than its recall.,11187
+Wine_decision_tree.png,The accuracy for the presented tree is higher than its precision.,8097
+BankNoteAuthentication_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,9699
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than its precision.,11193
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than its precision.,11192
+diabetes_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,10220
+vehicle_decision_tree.png,The accuracy for the presented tree is higher than its precision.,4143
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,9721
+adult_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,92
+diabetes_decision_tree.png,The accuracy for the presented tree is higher than its precision.,10214
+heart_decision_tree.png,The precision for the presented tree is higher than its specificity.,2632
+vehicle_decision_tree.png,The specificity for the presented tree is higher than its recall.,4139
+Iris_decision_tree.png,The specificity for the presented tree is higher than its recall.,7580
+adult_decision_tree.png,The recall for the presented tree is lower than its precision.,90
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than its recall.,10211
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than its specificity.,9722
+heart_decision_tree.png,The precision for the presented tree is higher than its recall.,2619
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than its specificity.,9719
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is higher than its recall.,11186
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than its precision.,9713
+diabetes_decision_tree.png,The recall for the presented tree is higher than its specificity.,10221
+Breast_Cancer_decision_tree.png,The specificity for the presented tree is lower than its recall.,11189
+Wine_decision_tree.png,The specificity for the presented tree is lower than its precision.,8102
+Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than its accuracy.,11178
+adult_decision_tree.png,The recall for the presented tree is higher than its accuracy.,74
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,9718
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is higher than its precision.,9712
+vehicle_decision_tree.png,The accuracy for the presented tree is higher than its recall.,4137
+vehicle_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,4152
+Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than its specificity.,1712
+vehicle_decision_tree.png,The accuracy for the presented tree is lower than its precision.,4146
+Wine_decision_tree.png,The accuracy for the presented tree is lower than its precision.,8100
+adult_decision_tree.png,The specificity for the presented tree is lower than its recall.,85
+Churn_Modelling_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,2435
+Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than its recall.,1702
+vehicle_decision_tree.png,The recall for the presented tree is higher than its specificity.,4150
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,9705
+diabetes_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,10207
+diabetes_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,10223
+Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than its accuracy.,1695
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than its recall.,9711
+diabetes_decision_tree.png,The recall for the presented tree is higher than its precision.,10215
+Breast_Cancer_decision_tree.png,The precision for the presented tree is higher than its accuracy.,11179
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is higher than its recall.,9708
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than its recall.,1701
+diabetes_decision_tree.png,The precision for the presented tree is lower than its recall.,10212
+Breast_Cancer_decision_tree.png,The precision for the presented tree is lower than its recall.,11188
+Iris_decision_tree.png,The recall for the presented tree is higher than its precision.,7585
+Wine_decision_tree.png,The precision for the presented tree is lower than its accuracy.,8089
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than its accuracy.,9704
+heart_decision_tree.png,The precision for the presented tree is lower than its accuracy.,2616
+adult_boxplots.png,The existence of outliers is one of the problems to tackle in this dataset.,1509
+Iris_decision_tree.png,The accuracy for the presented tree is higher than its precision.,7584
+Wine_decision_tree.png,The recall for the presented tree is higher than its precision.,8098
+heart_decision_tree.png,The accuracy for the presented tree is higher than its recall.,2618
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is lower than its precision.,9716
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is higher than its recall.,9707
+adult_decision_tree.png,The recall for the presented tree is higher than its specificity.,93
+Iris_decision_tree.png,The precision for the presented tree is higher than its recall.,7579
+adult_decision_tree.png,The precision for the presented tree is higher than its accuracy.,75
+Wine_decision_tree.png,The recall for the presented tree is higher than its accuracy.,8085
+Iris_decision_tree.png,The recall for the presented tree is lower than its accuracy.,7575
+Churn_Modelling_decision_tree.png,The precision for the presented tree is lower than its accuracy.,1696
+Churn_Modelling_decision_tree.png,The recall for the presented tree is lower than its specificity.,1714
+Wine_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,8090
+heart_decision_tree.png,The specificity for the presented tree is lower than its precision.,2629
+adult_decision_tree.png,The recall for the presented tree is lower than its specificity.,96
+vehicle_decision_tree.png,The precision for the presented tree is higher than its specificity.,4151
+vehicle_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,4130
+diabetes_decision_tree.png,The specificity for the presented tree is higher than its precision.,10216
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than its recall.,9709
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,11199
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is lower than its accuracy.,1697
+Iris_decision_tree.png,The specificity for the presented tree is higher than its precision.,7586
+BankNoteAuthentication_overfitting_dt_acc_rec.png,The difference between recall and accuracy becomes smaller with the depth due to the overfitting phenomenon.,9768
+Wine_decision_tree.png,The recall for the presented tree is lower than its specificity.,8107
+Wine_decision_tree.png,The recall for the presented tree is lower than its precision.,8101
+Iris_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,7593
+vehicle_decision_tree.png,The recall for the presented tree is lower than its accuracy.,4134
+Breast_Cancer_decision_tree.png,The accuracy for the presented tree is higher than its specificity.,11196
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is higher than its recall.,1698
+vehicle_decision_tree.png,The precision for the presented tree is lower than its specificity.,4154
+Churn_Modelling_decision_tree.png,The accuracy for the presented tree is lower than its precision.,1707
+Wine_decision_tree.png,The recall for the presented tree is higher than its specificity.,8104
+Iris_decision_tree.png,The recall for the presented tree is lower than its specificity.,7594
+diabetes_decision_tree.png,The recall for the presented tree is higher than its accuracy.,10202
+Iris_decision_tree.png,The specificity for the presented tree is lower than its precision.,7589
+diabetes_overfitting_rf.png,The random forests results shown can be explained by the lack of diversity resulting from the number of features considered.,10201
+Iris_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,7574
+BankNoteAuthentication_decision_tree.png,The specificity for the presented tree is lower than its precision.,9717
+vehicle_decision_tree.png,The recall for the presented tree is lower than its specificity.,4153
+Wine_decision_tree.png,The precision for the presented tree is lower than its recall.,8095
+Wine_decision_tree.png,The specificity for the presented tree is higher than its precision.,8099
+Breast_Cancer_decision_tree.png,The recall for the presented tree is higher than its precision.,11191
+Churn_Modelling_decision_tree.png,The specificity for the presented tree is higher than its precision.,1706
+Wine_decision_tree.png,The precision for the presented tree is higher than its specificity.,8105
+Breast_Cancer_decision_tree.png,The recall for the presented tree is lower than its specificity.,11200
+adult_decision_tree.png,The precision for the presented tree is higher than its specificity.,94
+Wine_decision_tree.png,The specificity for the presented tree is higher than its accuracy.,8087
+adult_decision_tree.png,The accuracy for the presented tree is lower than its specificity.,95
+BankNoteAuthentication_decision_tree.png,The precision for the presented tree is lower than its recall.,9710
+BankNoteAuthentication_decision_tree.png,The accuracy for the presented tree is lower than its precision.,9715
+Churn_Modelling_decision_tree.png,The precision for the presented tree is higher than its accuracy.,1693
+Iris_decision_tree.png,The accuracy for the presented tree is lower than its precision.,7587
+heart_decision_tree.png,The precision for the presented tree is higher than its accuracy.,2613
+BankNoteAuthentication_decision_tree.png,The recall for the presented tree is higher than its accuracy.,9700
+Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,8080
+diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,10197
+Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,11176
+heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,2607
+Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,8079
+BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,9694
+diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,10196
+adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,72
+Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,1686
+vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,4125
+diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,10199
+heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,2610
+Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,8083
+adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,70
+Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,8082
+vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,4126
+Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,1687
+Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,7568
+Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,7569
+BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,9697
+diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,10200
+Wine_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,8081
+diabetes_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,10198
+Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,11173
+Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,7570
+BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,9698
+Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,1689
+BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,9696
+adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,71
+heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,2609
+BankNoteAuthentication_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,9695
+Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,11175
+Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,11172
+vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,4127
+adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,68
+Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,7566
+Iris_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,7567
+vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 5 nodes of depth.,4128
+Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,1690
+adult_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 3 nodes of depth.,69
+heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 2 nodes of depth.,2606
+vehicle_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 6 nodes of depth.,4129
+Breast_Cancer_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,11174
+heart_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,2608
+Churn_Modelling_overfitting_decision_tree.png,We are able to identify the existence of overfitting for decision tree models with more than 4 nodes of depth.,1688
+Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,1681
+vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,4124
+vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,4121
+adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,65
+adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,67
+diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,10194
+Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,7561
+diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,10192
+BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,9693
+adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,63
+diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,10191
+Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,7562
+adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,66
+heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,2603
+Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,8076
+diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,10193
+Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,11167
+BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,9691
+Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,11171
+BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,9689
+vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,4122
+Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,11170
+BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,9692
+Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,8078
+Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,11169
+Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,8077
+Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,1685
+heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,2602
+heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,2601
+Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,7565
+Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,1684
+Breast_Cancer_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,11168
+Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,1683
+heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,2605
+Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,7564
+adult_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,64
+BankNoteAuthentication_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,9690
+vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,4123
+diabetes_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 6 neighbors.,10195
+Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,8075
+Wine_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,8074
+Iris_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 4 neighbors.,7563
+Churn_Modelling_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 3 neighbors.,1682
+heart_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 5 neighbors.,2604
+vehicle_overfitting_knn.png,We are able to identify the existence of overfitting for KNN models with less than 2 neighbors.,4120
+Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 53 estimators.,11162
+diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 72 estimators.,10186
+Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 143 estimators.,1677
+adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 122 estimators.,60
+Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 89 estimators.,11166
+Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 54 estimators.,11163
+Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 59 estimators.,8069
+Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 113 estimators.,8071
+BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 113 estimators.,9686
+diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 146 estimators.,10188
+diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 134 estimators.,10187
+Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 116 estimators.,11164
+Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 109 estimators.,7559
+diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 111 estimators.,10190
+heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 52 estimators.,2597
+BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 88 estimators.,9685
+Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 102 estimators.,1679
+Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 147 estimators.,8070
+vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 69 estimators.,4116
+Breast_Cancer_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 142 estimators.,11165
+heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 131 estimators.,2598
+Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 76 estimators.,1676
+diabetes_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 56 estimators.,10189
+BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 82 estimators.,9688
+adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 102 estimators.,58
+vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 131 estimators.,4118
+Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 87 estimators.,1678
+Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 87 estimators.,7556
+adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 112 estimators.,61
+heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 56 estimators.,2599
+Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 54 estimators.,7560
+Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 55 estimators.,8073
+heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 139 estimators.,2600
+BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 125 estimators.,9687
+vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 143 estimators.,4119
+BankNoteAuthentication_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 132 estimators.,9684
+Wine_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 75 estimators.,8072
+Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 90 estimators.,7557
+Iris_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 97 estimators.,7558
+heart_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 74 estimators.,2596
+vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 91 estimators.,4115
+adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 95 estimators.,59
+vehicle_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 62 estimators.,4117
+Churn_Modelling_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 150 estimators.,1680
+adult_overfitting_rf.png,We are able to identify the existence of overfitting for random forest models with more than 91 estimators.,62
+Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 128 estimators.,8065
+Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 52 estimators.,11159
+vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 69 estimators.,4113
+heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 81 estimators.,2592
+Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 101 estimators.,8068
+diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 134 estimators.,10184
+adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 119 estimators.,56
+Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 96 estimators.,1672
+Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 67 estimators.,7553
+Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 73 estimators.,8066
+Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 98 estimators.,7554
+diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 56 estimators.,10185
+Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 107 estimators.,1674
+Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 65 estimators.,11160
+Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 63 estimators.,11157
+Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 72 estimators.,7552
+diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 103 estimators.,10182
+adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 133 estimators.,53
+heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 121 estimators.,2595
+vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 78 estimators.,4114
+BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 95 estimators.,9683
+Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 55 estimators.,7555
+heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 130 estimators.,2594
+Iris_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 148 estimators.,7551
+Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 102 estimators.,1673
+BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 88 estimators.,9679
+BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 70 estimators.,9680
+Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 57 estimators.,11158
+Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 99 estimators.,8064
+diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 91 estimators.,10183
+Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 122 estimators.,1671
+BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 117 estimators.,9681
+heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 112 estimators.,2593
+diabetes_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 119 estimators.,10181
+Breast_Cancer_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 148 estimators.,11161
+adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 146 estimators.,57
+Churn_Modelling_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 82 estimators.,1675
+Wine_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 129 estimators.,8067
+heart_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 83 estimators.,2591
+vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 122 estimators.,4110
+BankNoteAuthentication_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 99 estimators.,9682
+adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 76 estimators.,55
+adult_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 54 estimators.,54
+vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 59 estimators.,4112
+vehicle_overfitting_gb.png,We are able to identify the existence of overfitting for gradient boosting models with more than 128 estimators.,4111
+vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 735 episodes.,4108
+adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 566 episodes.,50
+Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1406 episodes.,8062
+vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1295 episodes.,4109
+Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 702 episodes.,8061
+Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1274 episodes.,1668
+vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 634 episodes.,4105
+heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1250 episodes.,2589
+Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1293 episodes.,1666
+Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 566 episodes.,7547
+BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 668 episodes.,9674
+Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1063 episodes.,11155
+Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 939 episodes.,7548
+BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1124 episodes.,9677
+diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1084 episodes.,10177
+heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 706 episodes.,2587
+Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1306 episodes.,11154
+vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 975 episodes.,4107
+Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 844 episodes.,11152
+Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 948 episodes.,8059
+diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 770 episodes.,10176
+diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1062 episodes.,10179
+adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 936 episodes.,52
+heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 964 episodes.,2586
+Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1420 episodes.,1669
+Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1206 episodes.,11156
+BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1448 episodes.,9676
+BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1297 episodes.,9675
+Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 598 episodes.,7546
+adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1498 episodes.,48
+Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1389 episodes.,8060
+Wine_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 631 episodes.,8063
+Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1188 episodes.,1667
+Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 767 episodes.,7550
+adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 717 episodes.,49
+diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1242 episodes.,10178
+adult_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 657 episodes.,51
+diabetes_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1369 episodes.,10180
+Iris_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 541 episodes.,7549
+vehicle_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 788 episodes.,4106
+Breast_Cancer_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 777 episodes.,11153
+BankNoteAuthentication_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 1164 episodes.,9678
+Churn_Modelling_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 627 episodes.,1670
+heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 932 episodes.,2588
+heart_overfitting_mlp.png,We are able to identify the existence of overfitting for MLP models trained longer than 852 episodes.,2590
+Iris_boxplots.png,Outliers seem to be a problem in the dataset.,7959
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 167.",10153
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 12.",8026
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 2.",4015
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 214.",9635
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A, not B), as Iris-virginica.",7496
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 2.",8004
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, not B) as 4.",4104
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (A,B) as <=50K.",40
+Wine_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",8601
+heart_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",3338
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as <=50K for any k ≤ 21974.",9
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 60.",8044
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 435.",4037
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as M.",11107
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 30.",4065
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",8002
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 30.",4066
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A,B) as 1.",4089
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 167.",10157
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as <=50K.",0
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as B for any k ≤ 144.",11142
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A, not B) as Iris-virginica.",7544
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 53.",4075
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 12.",8029
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (not A, B) as 0.",2579
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-versicolor for any k ≤ 35.",7504
+heart_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,4004
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 53.",4074
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 30.",4071
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",2544
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as >50K for any k ≤ 434.",37
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-setosa for any k ≤ 32.",7524
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (not A, not B) as M.",11147
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, B) as 3.",4098
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",10128
+heart_histograms_numeric.png,Outliers seem to be a problem in the dataset.,3877
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 214.",9634
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 131.",9663
+diabetes_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,10571
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",10129
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as >50K for any k ≤ 9274.",31
+diabetes_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",10736
+adult_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,337
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 53.",4073
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 137.",2567
+Iris_boxplots.png,Those boxplots show that the data is not normalized.,7972
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as <=50K.",3
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 12.",8033
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as B.",11111
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 12.",8023
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, not B) as 1.",8050
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 60.",8038
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 3.",8010
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A,B) as Iris-setosa.",7534
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (A,B) as 1.",10168
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 0.",1622
+vehicle_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,4684
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 111.",10138
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 137.",2565
+Wine_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,8600
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 436.",9643
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 436.",9646
+Churn_Modelling_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,2076
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 111.",10141
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, B) as 3.",8056
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (not A, not B) as >50K.",47
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 435.",4027
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 114.",1639
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (A,B) as 0.",2578
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as <=50K.",1
+vehicle_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",4723
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as <=50K.",2
+BankNoteAuthentication_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",9935
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 30.",4058
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 53.",4080
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A,B) as 3.",4097
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 124.",1653
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 4 for any k ≤ 74.",4053
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 131.",9662
+diabetes_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,10471
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 30.",4070
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as <=50K for any k ≤ 21974.",8
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 111.",10143
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 60.",8043
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as >50K for any k ≤ 21974.",13
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (A,B) as 0.",9666
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 124.",1654
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as <=50K for any k ≤ 9274.",25
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",10130
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 181.",2556
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 179.",9656
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",1618
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as Iris-virginica.",7497
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 4831.",1632
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A,B), as 3.",8007
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A,B) as Iris-virginica.",7542
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (A,B) as >50K.",44
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",4010
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 137.",2569
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (A, not B) as 0.",9668
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as >50K.",7
+Wine_boxplots.png,Outliers seem to be a problem in the dataset.,9444
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (A, not B) as >50K.",46
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 53.",4083
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 179.",9651
+Wine_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,8599
+Wine_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",8892
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 436.",9642
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (not A, B), as B.",11109
+Churn_Modelling_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,1952
+BankNoteAuthentication_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,9925
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as B for any k ≤ 184.",11118
+adult_mv.png,Dropping all records with missing values would be better than to drop the variables with missing values.,679
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 435.",4030
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, the Decision Tree presented classifies (A, not B) as 0.",10174
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 111.",10137
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",9632
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",4009
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-versicolor for any k ≤ 38.",7515
+Churn_Modelling_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",2188
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 435.",4028
+BankNoteAuthentication_boxplots.png,Those boxplots show that the data is not normalized.,10101
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, B) as 4.",4102
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 1.",1619
+BankNoteAuthentication_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",9963
+diabetes_boxplots.png,Outliers seem to be a problem in the dataset.,11008
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 4831.",1630
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 197.",2572
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 53.",4084
+diabetes_boxplots.png,Those boxplots show that the data is not normalized.,11065
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as B for any k ≤ 50.",11124
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (not A, B) as 0.",9667
+Iris_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",7806
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (not A, not B) as 0.",1665
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, B), as Iris-setosa.",7487
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 137.",2566
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (not A, B) as 2.",4094
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 1931.",1643
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 114.",1634
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",7999
+BankNoteAuthentication_nr_records_nr_variables.png,Balancing this dataset by SMOTE would most probably be preferable over undersampling.,9961
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (not A, not B) as 1.",9673
+Iris_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,7994
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 4 for any k ≤ 74.",4054
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 124.",1650
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 181.",2559
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 0.",2541
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A, not B), as Iris-versicolor.",7492
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-virginica for any k ≤ 38.",7521
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A, not B) as 4.",4103
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 181.",2558
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (A,B) as 2.",8051
+adult_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,1613
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (A, not B) as B.",11150
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 1.",4011
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 53.",4076
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 161.",10162
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 49.",8011
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 167.",10159
+Wine_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,8429
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 49.",8021
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 98.",10146
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 60.",8040
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A,B) as B for any k ≤ 184.",11116
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 436.",9648
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 2 for any k ≤ 49.",8017
+Breast_Cancer_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,12684
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as <=50K for any k ≤ 541.",17
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, B) as 2.",8052
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (A, not B), as B.",11110
+Iris_boxplots.png,Scaling this dataset would be mandatory to improve the results with distance-based methods.,7796
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 12.",8031
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A,B) as Iris-versicolor.",7538
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 114.",1635
+Iris_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",7834
+vehicle_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,5085
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as M for any k ≤ 144.",11138
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as >50K for any k ≤ 21974.",12
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 197.",2574
+vehicle_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",5677
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 12.",8028
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 181.",2555
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A,B) as Iris-versicolor for any k ≤ 38.",7514
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, B) as B for any k ≤ 50.",11125
+diabetes_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,10570
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as B for any k ≤ 20.",11135
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 124.",1656
+adult_histograms_numeric.png,Outliers seem to be a problem in the dataset.,1540
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A,B), as 2.",8003
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (not A, B) as 1.",1659
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 114.",1637
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (not A, B) as 1.",2583
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 30.",4067
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (A, not B) as Iris-versicolor.",7540
+Churn_Modelling_boxplots.png,Those boxplots show that the data is not normalized.,2497
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 2 for any k ≤ 53.",4077
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 435.",4025
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 2 for any k ≤ 74.",4046
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, the Decision Tree presented classifies (A,B) as 1.",9670
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 1 for any k ≤ 214.",9640
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 74.",4044
+BankNoteAuthentication_nr_records_nr_variables.png,We face the curse of dimensionality when training a classifier with this dataset.,10123
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 435.",4034
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as <=50K for any k ≤ 9274.",27
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 1931.",1649
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (not A, B) as M.",11145
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 0.",2538
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, the Decision Tree presented classifies (not A, B) as B.",11149
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, the Decision Tree presented classifies (A, not B) as 1.",1660
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as <=50K for any k ≤ 21974.",11
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, the Decision Tree presented classifies (not A, not B) as 2.",8054
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 435.",4036
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (A, not B) as Iris-virginica for any k ≤ 35.",7508
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",4012
+adult_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",338
+adult_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",1019
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-setosa for any k ≤ 38.",7513
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 0 for any k ≤ 131.",9659
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (A, not B) as M for any k ≤ 50.",11122
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (not A, B), as Iris-versicolor.",7491
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 74.",4052
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A, not B) as 2.",4095
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, not B) as 3 for any k ≤ 60.",8046
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 1.",1621
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 435.",4033
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as M for any k ≤ 184.",11115
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 2.",8005
+Iris_boxplots.png,Normalization of this dataset could not have impact on a KNN classifier.,7805
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 4.",4023
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 167.",10152
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 202.",2551
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, B) as Iris-virginica.",7543
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 202.",2549
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 98.",10144
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 0.",9627
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-virginica for any k ≤ 32.",7531
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, not B) as Iris-versicolor for any k ≤ 35.",7505
+Churn_Modelling_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",2077
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-setosa for any k ≤ 38.",7511
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, not B) as <=50K for any k ≤ 434.",35
+Iris_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",7901
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that KNN algorithm classifies (not A, B) as Iris-versicolor for any k ≤ 32.",7527
+vehicle_boxplots.png,Those boxplots show that the data is not normalized.,7416
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A, not B) as 0 for any k ≤ 436.",9644
+Churn_Modelling_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",1953
+heart_scatter-plots.png,Balancing this dataset by SMOTE would be riskier than oversampling by replication.,3124
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 1 for any k ≤ 74.",4041
+heart_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,3122
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",2542
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A, not B) as 4 for any k ≤ 53.",4087
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, the Decision Tree presented classifies (A,B) as 1.",2582
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, not B), as 3.",4020
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 12.",8032
+Iris_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,7831
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (A, not B), as >50K.",6
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 4 for any k ≤ 53.",4088
+BankNoteAuthentication_correlation_heatmap.png,"From the correlation analysis alone, it is clear that there are relevant variables.",10030
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that Naive Bayes algorithm classifies (A, not B), as M.",11106
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (not A, B) as >50K for any k ≤ 9274.",29
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that Naive Bayes algorithm classifies (A, not B), as 3.",8009
+Wine_class_histogram.png,Balancing this dataset would be mandatory to improve the results.,8598
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, B) as 3 for any k ≤ 74.",4050
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 4831.",1627
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 131.",9658
+Breast_Cancer_decision_tree.png,"Considering that A=True<=>perimeter_mean<=90.47 and B=True<=>texture_worst<=27.89, it is possible to state that KNN algorithm classifies (not A, not B) as M for any k ≤ 20.",11131
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as 2.",4014
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as >50K for any k ≤ 21974.",14
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 179.",9657
+diabetes_correlation_heatmap.png,"Applying a non-supervised feature selection based on the redundancy, would not increase the performance of the generality of the training algorithms in this dataset.",10573
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (not A, not B) as 2 for any k ≤ 30.",4064
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (A, not B) as 3 for any k ≤ 60.",8045
+Wine_decision_tree.png,"Considering that A=True<=>Total phenols<=2.36 and B=True<=>Proanthocyanins<=1.58, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 49.",8012
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, it is possible to state that KNN algorithm classifies (A,B) as 3 for any k ≤ 53.",4081
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, the Decision Tree presented classifies (not A, B) as <=50K.",41
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (A,B) as 0 for any k ≤ 202.",2546
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 436.",9647
+diabetes_decision_tree.png,"Considering that A=True<=>BMI<=29.85 and B=True<=>Age<=27.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 98.",10147
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that Naive Bayes algorithm classifies (A,B), as 1.",9630
+Wine_boxplots.png,"Multiplying ratio and Boolean variables by 100, and variables with a range between 0 and 10 by 10, would have an impact similar to other scaling transformations.",8454
+heart_decision_tree.png,"Considering that A=True<=>slope<=1.5 and B=True<=>restecg<=0.5, it is possible to state that KNN algorithm classifies (not A, not B) as 1 for any k ≤ 197.",2577
+Churn_Modelling_decision_tree.png,"Considering that A=True<=>Age<=42.5 and B=True<=>NumOfProducts<=2.5, it is possible to state that KNN algorithm classifies (not A, not B) as 0 for any k ≤ 124.",1657
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as <=50K for any k ≤ 21974.",10
+vehicle_decision_tree.png,"Considering that A=True<=>LENGTHRECTANGULAR<=165.5 and B=True<=>PR AXIS ASPECT RATIO<=67.5, the Decision Tree presented classifies (A,B) as 2.",4093
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A,B) as <=50K for any k ≤ 434.",32
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, the Decision Tree presented classifies (not A, B) as Iris-versicolor.",7539
+Iris_decision_tree.png,"Considering that A=True<=>PetalWidthCm<=0.7 and B=True<=>PetalWidthCm<=1.75, it is possible to state that Naive Bayes algorithm classifies (A,B), as Iris-versicolor.",7490
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that Naive Bayes algorithm classifies (not A, B), as >50K.",5
+adult_decision_tree.png,"Considering that A=True<=>hours-per-week<=41.5 and B=True<=>capital-loss<=1820.5, it is possible to state that KNN algorithm classifies (A, not B) as >50K for any k ≤ 9274.",30
+BankNoteAuthentication_decision_tree.png,"Considering that A=True<=>skewness<=5.16 and B=True<=>curtosis<=0.19, it is possible to state that KNN algorithm classifies (not A, B) as 1 for any k ≤ 179.",9655