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（GAMs）因其通过将目标值表示为特征非线性变换之和而具有的可解释性，近期再次备受关注。尽管当前对GAMs热情高涨，但其对共曲性（即特征之间可能存在的非线性依赖关系）的敏感性至今在很大程度上被忽视。本文中，我们展示了共曲性如何严重损害GAMs的可解释性，并提出了一种补救措施：一种概念简单但有效的正则化器，用于惩罚非线性变换后特征变量之间的成对相关性。该过程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我抵消导致的歧义来增强可解释性。我们在合成数据集以及真实世界的时间序列和表格数据集上验证了该正则化器的有效性。实验表明，在不显著影响预测质量的前提下，可降低GAMs中的共曲性，从而提升可解释性并减少特征重要性中的方差。