     mask = curr_samples

        # XXX todo: idem without dataframe

        curr_features_to_use = np.unique(curr_features)
        X_oob = pd.DataFrame(
            (X[mask, :])[:, curr_features_to_use],
            columns=np.array(feature_names)[curr_features_to_use]
        )

        if X_oob.shape[1] &lt;= 1:  # otherwise pandas bug (cf. issue #16363)
            return []

        y_oob = y[mask]
        y_oob = np.array((y_oob != 0))

        # Add OOB performances to rules:
        scored_rules +