Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features. Despite the current enthusiasm for GAMs, their susceptibility to concurvity - i.e., (possibly non-linear) dependencies between the features - has hitherto been largely overlooked. Here, we demonstrate how concurvity can severly impair the interpretability of GAMs and propose a remedy: a conceptually simple, yet effective regularizer which penalizes pairwise correlations of the non-linearly transformed feature variables. This procedure is applicable to any differentiable additive model, such as Neural Additive Models or NeuralProphet, and enhances interpretability by eliminating ambiguities due to self-canceling feature contributions. We validate the effectiveness of our regularizer in experiments on synthetic as well as real-world datasets for time-series and tabular data. Our experiments show that concurvity in GAMs can be reduced without significantly compromising prediction quality, improving interpretability and reducing variance in the feature importances.

翻译：广义加性模型近年来因其可解释性而重新受到关注，这种可解释性源于将目标值表示为特征非线性变换之和。尽管当前对GAMs热情高涨，但模型对共曲线性（即特征之间可能存在的非线性依赖关系）的敏感性迄今仍被严重忽视。本文展示了共曲线性如何严重损害GAMs的可解释性，并提出一种解决方案：一个概念简单但有效的正则化器，通过惩罚非线性变换后特征变量的成对相关性来改进模型。该方法适用于任何可微加性模型（如神经加性模型或NeuralProphet），并通过消除因特征贡献自抵消而产生的歧义来增强可解释性。我们在合成数据集和真实世界数据集（涵盖时间序列和表格数据）上验证了该正则化器的有效性。实验表明，该方法可以在不显著降低预测质量的前提下减少GAMs中的共曲线性，从而提升可解释性并降低特征重要性的方差。