The analysis of variance (ANOVA) decomposition offers a systematic method to understand the interaction effects that contribute to a specific decision output. In this paper we introduce Neural-ANOVA, an approach to decompose neural networks into glassbox models using the ANOVA decomposition. Our approach formulates a learning problem, which enables rapid and closed-form evaluation of integrals over subspaces that appear in the calculation of the ANOVA decomposition. Finally, we conduct numerical experiments to illustrate the advantages of enhanced interpretability and model validation by a decomposition of the learned interaction effects.

翻译：方差分析（ANOVA）分解为理解影响特定决策输出的交互效应提供了系统化方法。本文提出神经方差分析（Neural-ANOVA），该方法利用方差分析分解将神经网络解构为透明盒模型。我们的方法构建了一个学习问题，能够对出现在方差分析分解计算过程中的子空间积分进行快速闭式求解。最后，我们通过数值实验展示了该方法在增强可解释性方面的优势，并通过对已学习交互效应的分解实现了模型验证。