def decision_function(self, X):
        """Predict confidence scores for samples.
        The confidence score for a sample is proportional to the signed
        distance of that sample to the hyperplane.

        Parameters
        ----------
        X : pandas.DataFrame (n_samples, n_features)
            The data matrix for which we want to get the confidence scores.

        Returns
        -------
        scores : array of shape (n_samples, n_classes)
            Confidence scores per (n_samples, n_classes) combination.
        """
        X_t = self._transform(
            X=X, metric=None, metric_special=self._metric_special,
            metric_missing=self._metric_missing)

        return self.estimator_.decision_function(X_t)
Predict confidence scores for samples.
        The confidence score for a sample is proportional to the signed
        distance of that sample to the hyperplane.

        Parameters
        ----------
        X : pandas.DataFrame (n_samples, n_features)
            The data matrix for which we want to get the confidence scores.

        Returns
        -------
        scores : array of shape (n_samples, n_classes)
            Confidence scores per (n_samples, n_classes) combination.
        
optbinning/scorecard/scorecard.py