  Today, as increasingly complex predictive models are developed, simple rule
sets remain a crucial tool to obtain interpretable predictions and drive
high-stakes decision making. However, a single rule set provides a partial
representation of a learning task. An emerging paradigm in interpretable
machine learning aims at exploring the Rashomon set of all models exhibiting
near-optimal performance. Existing work on Rashomon-set exploration focuses on
exhaustive search of the Rashomon set for particular classes of models, which
can be a computationally challenging task. On the other hand, exhaustive
enumeration leads to redundancy that often is not necessary, and a
representative sample or an estimate of the size of the Rashomon set is
sufficient for many applications. In this work, we propose, for the first time,
efficient methods to explore the Rashomon set of rule set models with or
without exhaustive search. Extensive experiments demonstrate the effectiveness
of the proposed methods in a variety of scenarios.
