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>