Reluctant generalized additive modeling
This work addresses the problem of building more accurate predictive models for data analysts when linear assumptions fail, offering a scalable solution for sparse GAMs across multiple data types, though it appears incremental as it builds on prior reluctant interaction modeling.
The authors tackled the challenge of scaling sparse generalized additive models (GAMs) for non-linear data by proposing a multi-stage algorithm called reluctant generalized additive modeling (RGAM), which prioritizes linear features and extends to various data types like binary and count data, demonstrating effectiveness in real and simulated examples.
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.