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Article Dans Une Revue Oxford Bulletin of Economics and Statistics Année : 2023

Interpretable Machine Learning Using Partial Linear Models*

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.
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Dates et versions

hal-04529011 , version 1 (02-04-2024)

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Emmanuel Flachaire, Sullivan Hué, Sébastien Laurent, Gilles Hacheme. Interpretable Machine Learning Using Partial Linear Models*. Oxford Bulletin of Economics and Statistics, 2023, ⟨10.1111/obes.12592⟩. ⟨hal-04529011⟩
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