  Sparse generalized additive models (GAMs) are an extension of sparse
generalized linear models which 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 modeling (Yu et
al. 2019), we propose a multi-stage algorithm, called $\textit{reluctant
generalized additive modeling (RGAM)}$, that can fit sparse generalized
additive models 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.
