def confusion_matrix(self, metrics=None, thresholds=None, train=False, valid=False, xval=False):
        """
        Get the confusion matrix for the specified metrics/thresholds.

        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 metrics: A list of metrics or a single metric among ``"min_per_class_accuracy"``, ``"absolute_mcc"``,
            ``"tnr"``, ``"fnr"``, ``"fpr"``, ``"tpr"``, ``"precision"``, ``"accuracy"``, ``"f0point5"``, ``"f2"``,
            ``"f1"``.
        :param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds
            in this set of metrics will be used.
        :param bool train: If train is True, then return the confusion matrix value for the training data.
        :param bool valid: If valid is True, then return the confusion matrix value for the validation data.
        :param bool xval:  If xval is True, then return the confusion matrix value for the cross validation data.
        :returns: The confusion matrix for this binomial model.
        """
        return {model.model_id: model.confusion_matrix(metrics, thresholds, train, valid, xval) for model in
                self.models}
def confusion_matrix(self, metrics=None, thresholds=None, train=False, valid=False, xval=False):
        """
        Get the confusion matrix for the specified metrics/thresholds.

        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 metrics: A list of metrics or a single metric among ``"min_per_class_accuracy"``, ``"absolute_mcc"``,
            ``"tnr"``, ``"fnr"``, ``"fpr"``, ``"tpr"``, ``"precision"``, ``"accuracy"``, ``"f0point5"``, ``"f2"``,
            ``"f1"``.
        :param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds
            in this set of metrics will be used.
        :param bool train: If train is True, then return the confusion matrix value for the training data.
        :param bool valid: If valid is True, then return the confusion matrix value for the validation data.
        :param bool xval:  If xval is True, then return the confusion matrix value for the cross validation data.
        :returns: The confusion matrix for this binomial model.
        """
        return {model.model_id: model.confusion_matrix(metrics, thresholds, train, valid, xval) for model in
                self.models}
def confusion_matrix ( self , metrics = None , thresholds = None , train = False , valid = False , xval = False ) : return { model . model_id : model . confusion_matrix ( metrics , thresholds , train , valid , xval ) for model in self . models }
Get the confusion matrix for the specified metrics/thresholds.

        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 metrics: A list of metrics or a single metric among ``"min_per_class_accuracy"``, ``"absolute_mcc"``,
            ``"tnr"``, ``"fnr"``, ``"fpr"``, ``"tpr"``, ``"precision"``, ``"accuracy"``, ``"f0point5"``, ``"f2"``,
            ``"f1"``.
        :param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the thresholds
            in this set of metrics will be used.
        :param bool train: If train is True, then return the confusion matrix value for the training data.
        :param bool valid: If valid is True, then return the confusion matrix value for the validation data.
        :param bool xval:  If xval is True, then return the confusion matrix value for the cross validation data.
        :returns: The confusion matrix for this binomial model.
Get the confusion matrix for the specified metrics / thresholds .