(labels, pred_scores)
return regression_metric.RMSE.compute(labels, pred_scores)
Compute the root of mean square 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.
        Return
        ----------
        float
            A positive floating point value
Compute the root of mean square 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.
        Return
        ----------
        float
            A positive floating point value
Compute the root of mean square 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 . Return ---------- float A positive floating point value
def root_mean_squared_error(labels, pred_scores):
        """
        Compute the root of mean square 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.
        Return
        ----------
        float
            A positive floating point value
        """
        return regression_metric.RMSE.compute(labels, pred_scores)
def root_mean_squared_error ( labels , pred_scores ) : return regression_metric . RMSE . compute ( labels , pred_scores )