How Interpretable and Trustworthy are GAMs?
Generalized additive models (GAMs) are useful for data bias discovery and model auditing. But do they always tell the true story of your data, or just its own hallucinated patterns? Also, which GAM algorithm is more accurate and less lying? In this paper we benchmark total 7 different GAMs variants and conclude that tree-based models are more trustworthy. We also design several metrics to decide which GAM is better.
TLDR: We compared total 7 different GAMs and showed which GAM is more trustworthy.
Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, Rich Caruana
Accepted in 2021 KDD