NODE-GAM: Neural Generalized Additive Model for Interpretable Deep
Learning
- URL: http://arxiv.org/abs/2106.01613v1
- Date: Thu, 3 Jun 2021 06:20:18 GMT
- Title: NODE-GAM: Neural Generalized Additive Model for Interpretable Deep
Learning
- Authors: Chun-Hao Chang, Rich Caruana, Anna Goldenberg
- Abstract summary: Generalized Additive Models (GAMs) have a long history of use in high-risk domains.
We propose a neural GAM (NODE-GAM) and neural GA$2$M (NODE-GA$2$M)
We show that our proposed models have comparable accuracy to other non-interpretable models, and outperform other GAMs on large datasets.
- Score: 16.15084484295732
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deployment of machine learning models in real high-risk settings (e.g.
healthcare) often depends not only on model's accuracy but also on its
fairness, robustness and interpretability. Generalized Additive Models (GAMs)
have a long history of use in these high-risk domains, but lack desirable
features of deep learning such as differentiability and scalability. In this
work, we propose a neural GAM (NODE-GAM) and neural GA$^2$M (NODE-GA$^2$M) that
scale well to large datasets, while remaining interpretable and accurate. We
show that our proposed models have comparable accuracy to other
non-interpretable models, and outperform other GAMs on large datasets. We also
show that our models are more accurate in self-supervised learning setting when
access to labeled data is limited.
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