# Efficient Exploration of the Rashomon Set of Rule Set Models

arXiv: https://arxiv.org/abs/2406.03059 · HTML: https://mlsanity.com/paper/2406.03059/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- ["How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations](https://mlsanity.com/paper/1911.06473/) (2019)
- [Optimal Sparse Decision Trees](https://mlsanity.com/paper/1904.12847/) (2019)
- [On the Existence of Simpler Machine Learning Models](https://mlsanity.com/paper/1908.01755/) (2019)
- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [Characterizing Fairness Over the Set of Good Models Under Selective Labels](https://mlsanity.com/paper/2101.00352/) (2021)
- [Learning Certifiably Optimal Rule Lists for Categorical Data](https://mlsanity.com/paper/1704.01701/) (2017)
- [Learning Optimized Or's of And's](https://mlsanity.com/paper/1511.02210/) (2015)
- [MLIC: A MaxSAT-Based framework for learning interpretable classification rules](https://mlsanity.com/paper/1812.01843/) (2018)
- [Diverse Rule Sets](https://mlsanity.com/paper/2006.09890/) (2020)
- [Computing the Collection of Good Models for Rule Lists](https://mlsanity.com/paper/2204.11285/) (2022)
- [A Scalable and Nearly Uniform Generator of SAT Witnesses](https://mlsanity.com/paper/1304.1584/) (2013)
- [Balancing Scalability and Uniformity in SAT Witness Generator](https://mlsanity.com/paper/1403.6246/) (2014)
- [Regularized impurity reduction: Accurate decision trees with complexity guarantees](https://mlsanity.com/paper/2208.10949/) (2022)
- [Constrained Counting and Sampling: Bridging the Gap between Theory and Practice](https://mlsanity.com/paper/1806.02239/) (2018)

## Built on by (most cited first, 10 of 10)

- [Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time](https://mlsanity.com/paper/2503.01087/) (2025)
- [Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective](https://mlsanity.com/paper/2412.09712/) (2024)
- [SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration](https://mlsanity.com/paper/2511.03344/) (2025)
- [Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization](https://mlsanity.com/paper/2502.05286/) (2025)
- [Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms](https://mlsanity.com/paper/2510.20621/) (2025)
- [CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters](https://mlsanity.com/paper/2603.15321/) (2026)
- [MOSS: Multi-Objective Optimization for Stable Rule Sets](https://mlsanity.com/paper/2506.08030/) (2025)
- [VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performance](https://mlsanity.com/paper/2507.22556/) (2025)
- [Efficient & Correct Predictive Equivalence for Decision Trees](https://mlsanity.com/paper/2509.17774/) (2025)
- [Falling Trees: A Model Class for Interpretable Risk Prioritization](https://mlsanity.com/paper/2609.23780/) (2026)

## Abstract (arXiv)

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.