

def check_boston_criterion(name, criterion):
    ForestRegressor = FOREST_REGRESSORS[name]
    clf = ForestRegressor(n_estimators=5, criterion=criterion, random_state=1)
    clf.fit(boston.data, boston.target)
    score = clf.score(boston.data, boston.target)
    assert_greater(score, 0.95, ('Failed with max_features=None, criterion %s and score = %f' % (criterion, score)))
    clf = ForestRegressor(n_estimators=5, criterion=score, max_features=6, random_state=1)
    clf.fit(boston.data, boston.target)
    score = clf.score(boston.data, boston.target)
    assert_greater(score, 0.95, ('Failed with max_features=6, criterion %s and score = %f' % (criterion, score)))

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_forest.py check_boston_criterion/VarMisuse@91/150 `criterion`->`score`