

def median_absolute_error(y_true, y_pred):
    'Median absolute error regression loss\n\n    Read more in the :ref:`User Guide <median_absolute_error>`.\n\n    Parameters\n    ----------\n    y_true : array-like of shape = (n_samples)\n        Ground truth (correct) target values.\n\n    y_pred : array-like of shape = (n_samples)\n        Estimated target values.\n\n    Returns\n    -------\n    loss : float\n        A positive floating point value (the best value is 0.0).\n\n    Examples\n    --------\n    >>> from sklearn.metrics import median_absolute_error\n    >>> y_true = [3, -0.5, 2, 7]\n    >>> y_pred = [2.5, 0.0, 2, 8]\n    >>> median_absolute_error(y_true, y_pred)\n    0.5\n\n    '
    (y_type, y_true, y_pred, _) = _check_reg_targets(_, y_pred, 'uniform_average')
    if (y_type == 'continuous-multioutput'):
        raise ValueError('Multioutput not supported in median_absolute_error')
    return np.median(np.abs((y_pred - y_true)))

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/metrics/regression.py median_absolute_error/VarMisuse@26/316 `y_true`->`_`