  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, we
propose in this paper to use partial linear models that are inherently
interpretable. Specifically, this article 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
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 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.
