README.md

metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-regression
model_file: model.pkl
widget:
  structuredData:
    Fedu:
      - 3
      - 3
      - 3
    Fjob:
      - other
      - other
      - services
    G1:
      - 12
      - 13
      - 8
    G2:
      - 13
      - 14
      - 7
    G3:
      - 12
      - 14
      - 0
    Medu:
      - 3
      - 2
      - 1
    Mjob:
      - services
      - other
      - at_home
    Pstatus:
      - T
      - T
      - T
    Walc:
      - 2
      - 1
      - 1
    absences:
      - 2
      - 0
      - 0
    activities:
      - 'yes'
      - 'no'
      - 'yes'
    address:
      - U
      - U
      - U
    age:
      - 16
      - 16
      - 16
    failures:
      - 0
      - 0
      - 3
    famrel:
      - 4
      - 5
      - 4
    famsize:
      - GT3
      - GT3
      - GT3
    famsup:
      - 'no'
      - 'no'
      - 'no'
    freetime:
      - 2
      - 3
      - 3
    goout:
      - 3
      - 3
      - 5
    guardian:
      - mother
      - father
      - mother
    health:
      - 3
      - 3
      - 3
    higher:
      - 'yes'
      - 'yes'
      - 'yes'
    internet:
      - 'yes'
      - 'yes'
      - 'yes'
    nursery:
      - 'yes'
      - 'yes'
      - 'no'
    paid:
      - 'yes'
      - 'no'
      - 'no'
    reason:
      - home
      - home
      - home
    romantic:
      - 'yes'
      - 'no'
      - 'yes'
    school:
      - GP
      - GP
      - GP
    schoolsup:
      - 'no'
      - 'no'
      - 'no'
    sex:
      - M
      - M
      - F
    studytime:
      - 2
      - 1
      - 2
    traveltime:
      - 1
      - 2
      - 1

Model description

This is an XGBoost model trained to predict daily alcohol consumption of students.

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

Click to expand

Hyperparameter	Value
memory
steps	[('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=5, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=None, ...))]
verbose	False
onehotencoder	OneHotEncoder(handle_unknown='ignore', sparse=False)
xgbregressor	XGBRegressor(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=5, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=None, ...)
onehotencoder__categories	auto
onehotencoder__drop
onehotencoder__dtype	<class 'numpy.float64'>
onehotencoder__handle_unknown	ignore
onehotencoder__sparse	False
xgbregressor__objective	reg:squarederror
xgbregressor__base_score
xgbregressor__booster
xgbregressor__callbacks
xgbregressor__colsample_bylevel
xgbregressor__colsample_bynode
xgbregressor__colsample_bytree
xgbregressor__early_stopping_rounds
xgbregressor__enable_categorical	False
xgbregressor__eval_metric
xgbregressor__feature_types
xgbregressor__gamma
xgbregressor__gpu_id
xgbregressor__grow_policy
xgbregressor__importance_type
xgbregressor__interaction_constraints
xgbregressor__learning_rate
xgbregressor__max_bin
xgbregressor__max_cat_threshold
xgbregressor__max_cat_to_onehot
xgbregressor__max_delta_step
xgbregressor__max_depth	5
xgbregressor__max_leaves
xgbregressor__min_child_weight
xgbregressor__missing	nan
xgbregressor__monotone_constraints
xgbregressor__n_estimators	100
xgbregressor__n_jobs
xgbregressor__num_parallel_tree
xgbregressor__predictor
xgbregressor__random_state
xgbregressor__reg_alpha
xgbregressor__reg_lambda
xgbregressor__sampling_method
xgbregressor__scale_pos_weight
xgbregressor__subsample
xgbregressor__tree_method
xgbregressor__validate_parameters
xgbregressor__verbosity

Model Plot

The model plot is below.

Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=5, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])

Please rerun this cell to show the HTML repr or trust the notebook.

Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
R squared	0.382
Mean Squared Error	0.43055

Feature Importance Plot

Explained as: feature importances

XGBoost feature importances; values are numbers 0 <= x <= 1;all values sum to 1.

Weight	Feature
0.3592	x26_5
0.0499	x26_1
0.0383	x26_4
0.0325	x23_3
0.0256	x28_0
0.0229	x30_10
0.0222	x8_health
0.0203	x29_10
0.0200	x14_2
0.0200	x7_3
0.0199	x31_16
0.0179	x28_8
0.0155	x28_6
0.0155	x11_mother
0.0149	x29_12
0.0145	x26_2
0.0138	x21_no
0.0112	x6_2
0.0098	x14_0
0.0092	x18_no
… 161 more …