h2o-py/h2o/model/multinomial.py
def hit_ratio_table(self, train=False, valid=False, xval=False):
        """
        Retrieve the Hit Ratios.

        If all are False (default), then return the training metric value.
        If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
        "valid", and "xval".

        :param train: If train is True, then return the hit ratio value for the training data.
        :param valid: If valid is True, then return the hit ratio value for the validation data.
        :param xval:  If xval is True, then return the hit ratio value for the cross validation data.
        :return: The hit ratio for this regression model.
        """
        tm = ModelBase._get_metrics(self, train, valid, xval)
        m = {}
        for k, v in zip(list(tm.keys()), list(tm.values())): m[k] = None if v is None else v.hit_ratio_table()
        return list(m.values())[0] if len(m) == 1 else m
def hit_ratio_table(self, train=False, valid=False, xval=False):
        """
        Retrieve the Hit Ratios.

        If all are False (default), then return the training metric value.
        If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
        "valid", and "xval".

        :param train: If train is True, then return the hit ratio value for the training data.
        :param valid: If valid is True, then return the hit ratio value for the validation data.
        :param xval:  If xval is True, then return the hit ratio value for the cross validation data.
        :return: The hit ratio for this regression model.
        """
        tm = ModelBase._get_metrics(self, train, valid, xval)
        m = {}
        for k, v in zip(list(tm.keys()), list(tm.values())): m[k] = None if v is None else v.hit_ratio_table()
        return list(m.values())[0] if len(m) == 1 else m
Retrieve the Hit Ratios.

        If all are False (default), then return the training metric value.
        If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
        "valid", and "xval".

        :param train: If train is True, then return the hit ratio value for the training data.
        :param valid: If valid is True, then return the hit ratio value for the validation data.
        :param xval:  If xval is True, then return the hit ratio value for the cross validation data.
        :return: The hit ratio for this regression model.