def METHOD_NAME(setup, estimator, parameters):
    samples = [[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
    labels = [0, 1, -1]
    clf = estimator(**parameters).fit(samples, labels)
    if parameters["kernel"] == "knn":
        return  # unstable test; changes in k-NN ordering break it
    else:
        np.testing.assert_array_almost_equal(
            np.asarray(clf.label_distributions_[2]), np.array([0.5, 0.5]), 2
        )