Diverse Representative Rashomon Sets for Sparse Generalized Additive Models

The Rashomon set paradigm seeks to uncover the full collection of near-optimal predictive models from a given function class. Access to this set allows users to better understand predictive multiplicity, interact with models, and select those that best satisfy domain-specific constraints. For sparse generalized additive models (GAMs), however, existing approaches that approximate the Rashomon set only apply to restricted subsets of features, overlooking the fact that equally accurate models can rely on many different feature subsets. We address this gap by representing the Rashomon set through a maximally diverse collection of models. We define a Metropolis-Hastings style approach for sampling maximally diverse GAMs from the Rashomon set. Experiments show that our methods can produce substantially more diverse solutions across all tested diversity metrics, providing users with actionable alternatives that maintain similar predictive performance.