print
# <codecell>
params = clf.get_params()
params
# <codecell>
test_score = np.zeros((params['n_estimators'],), dtype=np.float64)
test_score
# <codecell>
for i, y_pred in enumerate(clf.staged_decision_function(X_test)):
test_score[i] = clf.loss_(y_test, y_pred)
test_score
# <codecell>
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.title('Deviance')
plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',
label='Training Set Deviance')
plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',
label='Test Set Deviance')
plt.legend(loc='upper right')
plt.xlabel('Boosting Iterations')
plt.ylabel('Deviance')
