Axiomatic Interpretability for Multiclass Additive Models
Axiomatic Interpretability for Multiclass Additive Models
Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining interpretable. In the first part of this paper, we generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multiclass setting, showing that …