predicted = clf.predict(X_test)
# <codecell>
clf.feature_importances_
# <codecell>
print "Mean Squared Error"
mse = mean_squared_error(y_test, predicted)
print("MSE: %.4f" % mse)
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>
