# Initialize XGBoost with GPU parameters
xgb_clf = xgb.XGBClassifier(
    objective='binary:logistic',
    tree_method='gpu_hist',
    predictor='gpu_predictor',
    use_label_encoder=False,
    eval_metric='auc'
)

# Configure GridSearchCV
grid_search = GridSearchCV(
    estimator=xgb_clf,
    param_grid=param_grid,
    scoring='roc_auc',
    cv=3,
    verbose=2,
    n_jobs=1,  # Essential for GPU training
    return_train_score=False
)

# Execute parameter search
grid_search.fit(X_train, y_train)

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