@InProceedings{chang2021how,
  title={How Interpretable and Trustworthy are GAMs?},
  author={Chang, Chun-Hao and Tan, Sarah and Lengerich, Ben and Goldenberg, Anna and Caruana, Rich},
  journal={Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  booktitle={Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2021},
  informal_venue = {KDD},
  abstract = {Generalized additive models (GAMs) have become a leading model class 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 a few 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 models.},
  url_paper = {https://dl.acm.org/doi/abs/10.1145/3447548.3467453},
  url_preprint={https://arxiv.org/abs/2006.06466},
  keywords={Interpretable, Generalized Additive Models}
}

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