(labels, pred_scores)
return regression_metric.MAE().compute(labels, pred_scores)
Compute mean absolute error
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
        Returns
        ----------
        float
            A non-negative floating point.
Compute mean absolute error
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
        Returns
        ----------
        float
            A non-negative floating point.
Compute mean absolute error Parameters ---------- labels : value list . The labels of data set . pred_scores : value list . The predict results of model . It should be corresponding to labels each data . Returns ---------- float A non - negative floating point .
def mean_absolute_error(labels, pred_scores):
        """
        Compute mean absolute error
        Parameters
        ----------
        labels: value list. The labels of data set.
        pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
        Returns
        ----------
        float
            A non-negative floating point.
        """
        return regression_metric.MAE().compute(labels, pred_scores)
def mean_absolute_error ( labels , pred_scores ) : return regression_metric . MAE ( ) . compute ( labels , pred_scores )