Sullivan Hué

Article de revue

Interpretable Machine Learning Using Partial Linear Models*

Emmanuel Flachaire, Sullivan Hué, Sébastien Laurent, Gilles Hacheme, Oxford Bulletin of Economics and Statistics, 12/2023

Résumé Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes which has raised concerns from practitioners and regulators. As an alternative, we suggest using partial linear models that are inherently interpretable. Specifically, we propose to combine parametric and non‐parametric functions to accurately capture linearities and non‐linearities prevailing between dependent and explanatory variables, and a variable selection procedure to control for overfitting issues. Estimation relies on a two‐step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of our approach on a regression problem.

Mots clés Machine leaning, Lasso, Autometrics, GAM

Publication HAL Google Scholar

Article de revue

Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects

Elena Ivona Dumitrescu, Sullivan Hué, Christophe Hurlin, Sessi Tokpavi, European Journal of Operational Research, Vol. 297, No. 3, pp. 1178-1192, 01/2022

Résumé In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in the credit risk industry mainly because the lack of interpretability of ensemble methods is incompatible with the requirements of financial regulators. In this paper, we propose a high-performance and interpretable credit scoring method called penalised logistic tree regression (PLTR), which uses information from decision trees to improve the performance of logistic regression. Formally, rules extracted from various short-depth decision trees built with original predictive variables are used as predictors in a penalised logistic regression model. PLTR allows us to capture non-linear effects that can arise in credit scoring data while preserving the intrinsic interpretability of the logistic regression model. Monte Carlo simulations and empirical applications using four real credit default datasets show that PLTR predicts credit risk significantly more accurately than logistic regression and compares competitively to the random forest method

Mots clés Risk management, Credit scoring, Machine learning, Interpretability, Econometrics

Publication HAL Google Scholar