Author
Listed:
- Emmanuel Flachaire
(AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, AMU - Aix Marseille Université, CNRS - Centre National de la Recherche Scientifique)
- Sullivan Hué
(AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, AMU - Aix Marseille Université, CNRS - Centre National de la Recherche Scientifique)
- Sébastien Laurent
(AMU - Aix Marseille Université, CNRS - Centre National de la Recherche Scientifique, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, Aix-Marseille Graduate School of Management)
- Gilles Hacheme
(AMU - Aix Marseille Université, CNRS - Centre National de la Recherche Scientifique, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)
Abstract
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.
Suggested Citation
Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2023.
"Interpretable Machine Learning Using Partial Linear Models,"
Post-Print
hal-04529011, HAL.
Handle:
RePEc:hal:journl:hal-04529011
DOI: 10.1111/obes.12592
Note: View the original document on HAL open archive server: https://hal.science/hal-04529011
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