# Extract best results
best_params = grid_search.best_params_
best_score = grid_search.best_score_
best_model = grid_search.best_estimator_

print(f"Best parameters: {best_params}")
print(f"Best cross-validation AUC: {best_score:.4f}")

# Test set validation
test_predictions = best_model.predict_proba(X_test)[:, 1]
test_auc = roc_auc_score(y_test, test_predictions)
print(f"Test set AUC: {test_auc:.4f}")

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