Generalized additive models (GAMs) are a leading model class for interpretable machine learning. GAMs were originally trained using smoothing splines. Recently, tree-based GAMs where shape functions are gradient-boosted ensembles of bagged trees were proposed (e.g. Explainable Boosting Machine). In this paper, we introduce a competing three-step GAM learning approach where we combine i) the knowledge of the way to split the covariates space brought by an Additive tree model (ATM), ii) an ensemble of predictive linear scores derived from Generalized linear models (GLMs) using a binning strategy based on the ATM, iii) a final GLM to have a prediction model that ensures auto-calibration. Numerical experiments illustrate the very good performances of our approach on several datasets compared to GAM with splines, EBM or GLM with binarsity penalization. A case-study in trade credit insurance is also provided.

Keywords: Additive tree ensembles, Auto-calibration, Generalized additive models, Generalized linear models, Partitioning methods, XAI.