Axiomatic Interpretability for Multiclass Additive Models

Understand

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, and show that this multiclass algorithm outperforms existing GAM learning algorithms and sometimes matches the performance of full complexity models such as gradient boosted trees.
• In the second part, we turn our attention to the interpretability of GAMs in the multiclass setting.
• Surprisingly, the natural interpretability of GAMs breaks down when there are more than two classes.