Deep neural networks (DNNs) have found successful applications in many fields, but their black-box nature hinders interpretability. This is addressed by the neural additive model (NAM), in which the network is divided into additive sub-networks, thus making apparent the interaction between input features and predictions. In this paper, we approach the additive structure from a Bayesian perspective and develop a practical Laplace approximation. This enhances interpretability in three primary ways: a) It provides credible intervals for the recovered feature interactions by estimating function-space uncertainty of the sub-networks; b) it yields a tractable estimate of the marginal likelihood, which can be used to perform an implicit selection of features through an empirical Bayes procedure; and c) it can be used to rank feature pairs as candidates for second-order interactions in fine-tuned interaction models. We show empirically that our proposed Laplace-approximated NAM (LA-NAM) improves performance and interpretability on tabular regression and classification datasets and challenging real-world medical tasks.

本文从贝叶斯角度探讨神经相加模型，并开发了实用的拉普拉斯近似方法。研究结果表明，使用该方法得到的神经相加模型可以提高表格回归和分类数据集以及现实世界医学任务的性能和解释性。

拉普拉斯-近似神经加性模型:结合贝叶斯推理提高可解释性