xgboost python machine learning

set-up-hyperparameter-tuning.py

from skopt import BayesSearchCV
from skopt.space import Real, Categorical, Integer

search_space = {
    'clf__max_depth': Integer(2,8),
    'clf__learning_rate': Real(0.001, 1.0, prior='log-uniform'),
    'clf__subsample': Real(0.5, 1.0),
    'clf__colsample_bytree': Real(0.5, 1.0),
    'clf__colsample_bylevel': Real(0.5, 1.0),
    'clf__colsample_bynode' : Real(0.5, 1.0),
    'clf__reg_alpha': Real(0.0, 10.0),
    'clf__reg_lambda': Real(0.0, 10.0),
    'clf__gamma': Real(0.0, 10.0)
}

opt = BayesSearchCV(pipe, search_space, cv=3, n_iter=10, scoring='roc_auc', random_state=8) # in reality, you may consider setting cv and n_iter to higher values