Reluctant Generalised Additive Modelling

J. Kenneth Tay and Robert Tibshirani

International Statistical Review, 2020, vol. 88, issue S1, S205-S224

Abstract: Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non‐linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi‐stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non‐linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.

Date: 2020
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