Binary Classification for Enhancing Explainability of Combined Black-Box and White-Box Models

We propose a machine learning model that aims to balance the trade-off between explainability and predictive accuracy by combining black-box and white-box models. Our method uses an additional machine learning model to classify input data according to whether only the black-box model makes a correct prediction. Input data with a low probability of being correctly predicted by only the black-box model are allocated to the white-box model. Experimental results for 16 classification and 16 regression datasets show that our method can maintain overall accuracy and enhance explainability compared to an existing method. Our method is promising for improving the explainability of machine learning models in practical applications.