Papers › NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

3 Jun 2021ICLR 2022 4arXiv:2106.01613archive 2025-07-28

Chun-Hao Chang, Rich Caruana, Anna Goldenberg

Deployment of machine learning models in real high-risk settings (e.g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability. Generalized Additive Models (GAMs) are a class of interpretable models with a long history of use in these high-risk domains, but they lack desirable features of deep learning such as differentiability and scalability. In this work, we propose a neural GAM (NODE-GAM) and neural GA²M (NODE-GA²M) that scale well and perform better than other GAMs on large datasets, while remaining interpretable compared to other ensemble and deep learning models. We demonstrate that our models find interesting patterns in the data. Lastly, we show that we improve model accuracy via self-supervised pre-training, an improvement that is not possible for non-differentiable GAMs.

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