def METHOD_NAME():
    np.random.seed(1)
    n_classes = 3
    chunk = [i for i in range(50)]
    population_size = 10_000
    y_true = np.random.randint(0, n_classes, population_size)
    y_pred = np.random.randint(0, n_classes, population_size)
    lb = LabelBinarizer()
    y_true = lb.fit_transform(y_true)
    y_pred = lb.transform(y_pred)
    list_of_y_preds = list(y_pred.T)
    list_of_y_trues = list(y_true.T)
    components = mse.recall_sampling_error_components(list_of_y_trues, list_of_y_preds)
    sampling_error = mse.recall_sampling_error(components, chunk)
    assert np.round(sampling_error, 4) == 0.0668