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

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