Efficient Exploration of the Rashomon Set of Rule Set Models

TL;DR

This paper introduces efficient methods for exploring the Rashomon set of rule set models, enabling better understanding of near-optimal models without exhaustive search, which is computationally expensive.

Contribution

The work presents novel, efficient algorithms for Rashomon set exploration in rule set models, reducing computational costs compared to existing exhaustive methods.

Findings

Methods effectively explore the Rashomon set in various scenarios.

Proposed algorithms outperform exhaustive search in efficiency.

Exploration provides insights into model diversity and robustness.

Abstract

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.