Efficient Exploration of the Rashomon Set of Rule Set Models
This work addresses the need for interpretable machine learning in high-stakes decision-making by providing efficient ways to sample or estimate the Rashomon set, though it is incremental as it builds on existing exploration paradigms.
The paper tackles the problem of efficiently exploring the Rashomon set of rule set models, which includes all models with near-optimal performance, and proposes new methods that avoid exhaustive search, demonstrating effectiveness in various scenarios.
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.