def METHOD_NAME(n_instances, n_timepoints, n_intervals):
    """Check specific results."""
    X, _ = make_classification_problem(
        n_instances=n_instances, n_timepoints=n_timepoints, return_numpy=True
    )
    transformer = RandomIntervalFeatureExtractor(
        n_intervals=n_intervals, features=[np.mean, np.std]
    )
    Xt = transformer.fit_transform(X)
    Xt = Xt.loc[:, ~Xt.columns.duplicated()]
    # Check results
    intervals = transformer.intervals_
    for start, end in intervals:
        expected_mean = np.mean(X[:, 0, start:end], axis=-1)
        expected_std = np.std(X[:, 0, start:end], axis=-1)
        actual_means = Xt.loc[:, f"{start}_{end}_mean"].to_numpy().ravel()
        actual_stds = Xt.loc[:, f"{start}_{end}_std"].to_numpy().ravel()
        np.testing.assert_array_equal(actual_means, expected_mean)
        np.testing.assert_array_equal(actual_stds, expected_std)