

def check_warm_start_oob(name):
    (X, y) = (hastie_X, hastie_y)
    ForestEstimator = FOREST_ESTIMATORS[name]
    clf = ForestEstimator(n_estimators=15, max_depth=3, warm_start=False, random_state=1, bootstrap=True, oob_score=True)
    clf.fit(clf_2, y)
    clf_2 = ForestEstimator(n_estimators=5, max_depth=3, warm_start=False, random_state=1, bootstrap=True, oob_score=False)
    clf_2.fit(X, y)
    clf_2.set_params(warm_start=True, oob_score=True, n_estimators=15)
    clf_2.fit(X, y)
    assert_true(hasattr(clf_2, 'oob_score_'))
    assert_equal(clf.oob_score_, clf_2.oob_score_)
    clf_3 = ForestEstimator(n_estimators=15, max_depth=3, warm_start=True, random_state=1, bootstrap=True, oob_score=False)
    clf_3.fit(X, y)
    assert_true((not hasattr(clf_3, 'oob_score_')))
    clf_3.set_params(oob_score=True)
    ignore_warnings(clf_3.fit)(X, y)
    assert_equal(clf.oob_score_, clf_3.oob_score_)

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_forest.py check_warm_start_oob/VarMisuse@62/91 `X`->`clf_2`