A comparison of methods for the fitting of generalized additive models

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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Among different estimations of component functions and fitting procedures, Binder and Tutz (2008) found that boosting performed particularly well in high-dimensional settings, and Lou et al. (2012) found that using a shallow bagged ensemble of trees on a single feature in each step of stochastic gra”
Intelligible models for classification and regression (2012, methodology): “Binder and Tutz performed a comprehensive comparison of methods for fitting GAMs with regression splines [7].”
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models (2018, methodology): “c. Comparing several different GAM fitting procedures including backfitting and simultaneous optimization, Binder and Tutz found that boosting performed particularly well in high-dimensional settings [3]. Lou et al. developed the Explainable Boosting Machine (EBM) [20, 21] which boosts shallow bagge”
COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations (2020, methodology): “There are many approaches to estimating a GAM and we point the interested reader to Binder and Tutz [5].”