# Run the classifier clf.fit(X_train, y_train) # Identify training and test accuracy y_train_pred = clf.predict(X_train) y_test_pred = clf.predict(X_test) ada_train = accuracy_score(y_train, y_train_pred) ada_test = accuracy_score(y_test, y_test_pred) print('Ada boost train/test accuracies %.3f/%.3f' % (ada_train, ada_test)) # Draw learning curve drawLearningCurve(clf, X_train, X_test, y_train, y_test, min_size=2000, numpoints=10) plt.savefig('Boosting Learning Curve.png', bbox_inches='tight') # Print diagnostics print(clf.best_score_) print(clf.best_params_) print(clf.best_estimator_) print('gridscores:') print(clf.grid_scores_) scores = [x[1] for x in clf.grid_scores_] print('scores:') print(scores) scores = np.array(scores).reshape(len(parameters['learning_rate']),
# Identify training accuracy y_train_pred = clf.predict(X_transformed) train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0] print('Training accuracy: %.2f%%' % (train_acc * 100)) # Identify test set accuracy y_test_pred = clf.predict(X_testTransformed) test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0] print('Test accuracy: %.2f%%' % (test_acc * 100)) # Draw learning curve drawLearningCurve(clf, X_transformed, X_testTransformed, y_train, y_test, min_size=100, numpoints=50) plt.savefig('Neural Network Learning Curve.png', bbox_inches='tight') # Print diagnostics print(clf.best_score_) print(clf.best_params_) print(clf.best_estimator_) print('gridscores:') print(clf.grid_scores_) # Print diagnostics scores = [x[1] for x in clf.grid_scores_] print('scores:')