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