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