Explainable Machine Learning for Improving Logistic Regression Models

Model explainability has become an important objective when developing machine learning algorithms, especially in highly regulated industries. However, it is difficult to achieve both prediction accuracy and intrinsic explainability as the two objectives usually conflict with each other. Recent development regarding Explainable Neural Network, or xNN, shed some lights on resolving the trade-off between accuracy and explainability for neural network. In this paper, we propose an xNN approach to develop or improve logistic regressions, which can be useful in credit risk modeling and money-laundering or fraud detection. Our data experiment shows that the proposed xNN model keeps the flexibility of pursuing high prediction accuracy while attaining improved explainability.