hgboost

hgboost_classification_example_fit.py

# Fit
results = hgb.xgboost(X, y, pos_label=1, eval_metric='auc')
# results = hgb.catboost(X, y, pos_label=1, eval_metric='auc')
# results = hgb.lightboost(X, y, pos_label=1, eval_metric='auc')


# [hgboost] >Start hgboost classification.
# [hgboost] >Collecting xgb_clf parameters.
# [hgboost] >Correct for unbalanced classes using [scale_pos_weight]..
# [hgboost] >[13] hyperparameters in gridsearch space. Used loss function: [auc].
# [hgboost] >method: xgb_clf
# [hgboost] >eval_metric: auc
# [hgboost] >greater_is_better: True
# [hgboost] >*********************************************************************************
# [hgboost] >Total dataset: (891, 203) 
# [hgboost] >Validation set: (179, 203) 
# [hgboost] >Test-set: (278, 203) 
# [hgboost] >Train-set: (434, 203) 
# [hgboost] >*********************************************************************************
# [hgboost] >Searching across hyperparameter space for best performing parameters using maximum nr. evaluations: 250
# 100%|██████████| 250/250 [03:18<00:00,  1.26trial/s, best loss: -0.867519265453353] 
# [hgboost]> Collecting the hyperparameters from the [250] trials.
# [hgboost] >[auc]: 0.8675 Best performing model across 250 iterations using Bayesian Optimization with Hyperopt.
# [hgboost] >*********************************************************************************
# [hgboost] >5-fold cross validation for the top 10 scoring models, Total nr. tests: 50
# [hgboost] >[auc] (average): 0.8701 Best 5-fold CV model using optimized hyperparameters.
# [hgboost] >*********************************************************************************
# [hgboost] >Evaluate best [xgb_clf] model on validation dataset (179 samples, 20%)
# [hgboost] >[auc]: -0.8443 using optimized hyperparameters on validation set.
# [hgboost] >[auc]: -0.7912 using default (not optimized) parameters on validation set.
# [hgboost] >*********************************************************************************
# [hgboost] >Retrain [xgb_clf] on the entire dataset with the optimal hyperparameters.
# [hgboost] >Fin!