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Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models

Julien Siems · Konstantin Ditschuneit · Winfried Ripken · Alma Lindborg · Maximilian Schambach · Johannes Otterbach · Martin Genzel

Great Hall & Hall B1+B2 (level 1) #1525
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Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST


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.

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