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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