def METHOD_NAME(y_true, y_prob):
    """Compute binary logistic loss for classification.
    This is identical to log_loss in binary classification case,
    but is kept for its use in multilabel case.
    Parameters
    ----------
    y_true : array-like or label indicator matrix
        Ground truth (correct) labels.
    y_prob : array-like of float, shape = (n_samples, 1)
        Predicted probabilities, as returned by a classifier's
        predict_proba method.
    Returns
    -------
    loss : float
        The degree to which the samples are correctly predicted.
    """
    eps = np.finfo(y_prob.dtype).eps
    y_prob = np.clip(y_prob, eps, 1 - eps)
    return (
        -(xlogy(y_true, y_prob).sum() + xlogy(1 - y_true, 1 - y_prob).sum())
        / y_prob.shape[0]
    )