def mean_per_class_error(self, thresholds=None):
        """
        :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.
        :returns: mean per class error.
        """
        return [[x[0], 1 - x[1]] for x in self.metric("mean_per_class_accuracy", thresholds=thresholds)]
def mean_per_class_error(self, thresholds=None):
        """
        :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.
        :returns: mean per class error.
        """
        return [[x[0], 1 - x[1]] for x in self.metric("mean_per_class_accuracy", thresholds=thresholds)]
def mean_per_class_error ( self , thresholds = None ) : return [ [ x [ 0 ] , 1 - x [ 1 ] ] for x in self . metric ( "mean_per_class_accuracy" , thresholds = thresholds ) ]
: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.
        :returns: mean per class error.
: 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 . : returns : mean per class error .