Neural Additive and Basis Models with Feature Selection and Interactions

arXiv:2606.19850v1 Announce Type: new Abstract: Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability. The neural additive model (NAM) and its variant called the neural basis model (NBM) use neural networks (NNs) as nonlinear shape functions in generalized additive models (GAMs). Both models are highly interpretable and exhibit good performance and flexibility for NN training. NAM and NBM can provide and visualize the contribution of each feature to the prediction owing to GAM-based architectures. However, when using two-input N

Source: arXiv cs.LG — read the full report at the original publisher.

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