  Generalized additive models (GAMs) have become a leading modelclass for
interpretable machine learning. However, there are many algorithms for training
GAMs, and these can learn different or even contradictory models, while being
equally accurate. Which GAM should we trust? In this paper, we quantitatively
and qualitatively investigate a variety of GAM algorithms on real and simulated
datasets. We find that GAMs with high feature sparsity (only using afew
variables to make predictions) can miss patterns in the data and be unfair to
rare subpopulations. Our results suggest that inductive bias plays a crucial
role in what interpretable models learn and that tree-based GAMs represent the
best balance of sparsity, fidelity and accuracy and thus appear to be the most
trustworthy GAM.
