test_y = LabelEncoder().fit_transform(test_y) gb_tuned = GradientBoostingClassifier(random_state=1, learning_rate=0.05, n_estimators=500, max_depth=7, min_samples_split=2, min_samples_leaf=1, max_features=10, subsample=1) gb_tuned = PrecisionRecallCurve(gb_tuned, per_class=True, iso_f1_curves=True, fill_area=False, micro=False) gb_tuned.fit(train_x, train_y) ''' prediction_train = gb_tuned.predict(train_x) prediction_test = gb_tuned.predict(test_x) print('Training accuracy:', accuracy_score(train_y, prediction_train)) print('Testing accuracy:', accuracy_score(test_y, prediction_test)) print('Classification report:') print(classification_report(test_y, prediction_test)) print('Confusion matrix:') print(confusion_matrix(test_y, prediction_test)) ''' # Draw precision-recall curve gb_tuned.score(test_x, test_y) gb_tuned.poof()