2023/05/19 by Julien Siems, Konstantin Ditschuneit, Siems, Julien +12 · 1 voice · 4 citations
Computer Science · Mathematics · #Explainable Artificial Intelligence (XAI) #Machine Learning and Data Classification #Neural Networks and Applications #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2305.11475
arxiv published 2023/05/19 · arxiv updated 2023/11/25
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.