h2o-py/h2o/grid/metrics.py
def F1(self, thresholds=None, train=False, valid=False, xval=False):
        """
        Get the F1 values for a set of thresholds for the models explored.

        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 List thresholds: If None, then the thresholds in this set of metrics will be used.
        :param bool train: If True, return the F1 value for the training data.
        :param bool valid: If True, return the F1 value for the validation data.
        :param bool xval: If True, return the F1 value for each of the cross-validated splits.
        :returns: Dictionary of model keys to F1 values
        """
        return {model.model_id: model.F1(thresholds, train, valid, xval) for model in
                self.models}
def F1(self, thresholds=None, train=False, valid=False, xval=False):
        return {model.model_id: model.F1(thresholds, train, valid, xval) for model in
                self.models}
def F1 ( self , thresholds = None , train = False , valid = False , xval = False ) : return { model . model_id : model . F1 ( thresholds , train , valid , xval ) for model in self . models }
Get the F1 values for a set of thresholds for the models explored.

        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 List thresholds: If None, then the thresholds in this set of metrics will be used.
        :param bool train: If True, return the F1 value for the training data.
        :param bool valid: If True, return the F1 value for the validation data.
        :param bool xval: If True, return the F1 value for each of the cross-validated splits.
        :returns: Dictionary of model keys to F1 values
Get the F1 values for a set of thresholds for the models explored .