tree.fit(X_train_hastie, y_train_hastie)
print('Accuracy for the Decision Tree: {}'.format(
tree.score(X_test_hastie, y_test_hastie)))
gbc2_model = GradientBoostingClassifier(n_estimators=5000, learning_rate=0.01,
max_depth=3,
random_state=0)
gbc2_model.fit(X_train_hastie, y_train_hastie)
y_pred = gbc2_model.predict_proba(X_test_hastie)[:, 1]
print('Accuracy for Gradient Boosting: {}'.format(
gbc2_model.score(X_test_hastie, y_test_hastie)))
gbc2_cumulative_predictions = numpy.array(
[x for x in gbc2_model.staged_decision_function(X_test_hastie)])[:, :, 0]
print_loss(gbc2_cumulative_predictions, y_test_hastie)
xg_model = XGBClassifier(n_estimators=5000, learning_rate=0.01)
#print(xg_model)
xg_model.fit(X_train_hastie, y_train_hastie)
print('Accuracy for XGBoost: {}'.format(xg_model.score(X_test_hastie, y_test_hastie)))
y_pred = xg_model.predict_proba(X_test_hastie)[:, 1]
# Attetion: This will not work
xg_cumulative_predictions = numpy.array(
[x for x in xg_model.staged_decision_function(X_test_hastie)])[:, :, 0]
print_loss(xg_cumulative_loss)
