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）因其将目标值表示为特征非线性变换之和的可解释性，近来重新受到广泛关注。尽管当前对GAMs的热情高涨，但其对共曲性（即特征间可能存在的非线性依赖关系）的敏感性迄今在很大程度上被忽视。在此，我们展示了共曲性如何严重损害GAMs的可解释性，并提出一种补救措施：一个概念简单但有效的正则化器，该正则化器对非线性变换后特征变量的成对相关性进行惩罚。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相互抵消而导致的歧义来增强可解释性。我们在合成数据集和真实世界的时间序列及表格数据实验中验证了该正则化器的有效性。实验表明，共曲性在GAMs中可被显著降低而不牺牲预测质量，从而提高可解释性并减小特征重要性的方差。