ossible: doing it in-bag. Performance evaluation is &#34;
                    &#34;likely to be wrong (overfitting) and selected rules are likely to not &#34;
                    &#34;perform well! Please use max_samples &lt; 1.&#34;
                )
            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)[cu