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scikit-learn/
xgboost-example

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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.

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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 …

scikit-learn/xgboost-example