

@skip_if_32bit
def test_importances():
    (X, y) = datasets.make_classification(n_samples=500, n_features=10, n_informative=3, n_redundant=0, n_repeated=0, shuffle=False, random_state=0)
    for (name, criterion) in product(FOREST_CLASSIFIERS, ['gini', 'entropy']):
        (yield (check_importances, name, criterion, X, y))
    for (name, criterion) in product(FOREST_REGRESSORS, ['mse', 'friedman_mse']):
        (yield (check_importances, name, criterion, X, y))

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_forest.py test_importances/original