GAM(L)A: An econometric model for interpretable machine learning
Sullivan Hué ()
French Stata Users' Group Meetings 2022 from Stata Users Group
Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes or uninterpretable models, which has raised concerns from practitioners and regulators. As an alternative, I propose to use partial linear models that are inherently interpretable. Specifically, this presentation introduces GAM-lasso (GAMLA) and GAM-autometrics (GAMA), denoted as GAM(L)A in short. GAM(L)A combines parametric and non-parametric functions to accurately capture linearities and nonlinearities 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. I illustrate the predictive performance and interpretability of GAM(L)A on a regression and a classification problem. The results show that GAM(L)A outperforms parametric models augmented by quadratic, cubic, and interaction effects. Moreover, the results also suggest that the performance of GAM(L)A is not significantly different from that of random forest and gradient boosting.
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Persistent link: https://EconPapers.repec.org/RePEc:boc:fsug22:19
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