Estimates the accuracy of the predictions from the constructed feature

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix to predict from
        classes: array-like {n_samples}
            List of true class labels

        Returns
        -------
        accuracy_score: float
            The estimated accuracy based on the constructed feature

def score(self, features, classes, scoring_function=accuracy_score, **scoring_function_kwargs):
        """Estimates the accuracy of the predictions from the constructed feature

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix to predict from
        classes: array-like {n_samples}
            List of true class labels

        Returns
        -------
        accuracy_score: float
            The estimated accuracy based on the constructed feature

        """
        if not self.mu:
            raise ValueError('The DistanceClassifier model must be fit before score() can be called')

        return scoring_function(classes, self.predict(features), **scoring_function_kwargs)