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Amazing Things Come From Having Many Good Models

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arxiv 2407.04846 v2 pith:X2BHONWA submitted 2024-07-05 cs.LG cs.AI

Cynthia Rudin , Chudi Zhong , Lesia Semenova , Margo Seltzer , Ronald Parr , Jiachang Liu , Srikar Katta , Jon Donnelly
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Harry Chen Zachery Boner
This is my paper
classification cs.LGcs.AI
keywords effectrashomonmanymodelsaddressfairnessgoodlearning
verification ladder T0 review T1 audit T2 compute T3 formal
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The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many real datasets and when it does, it sparks both magic and consternation, but mostly magic. In light of the Rashomon Effect, this perspective piece proposes reshaping the way we think about machine learning, particularly for tabular data problems in the nondeterministic (noisy) setting. We address how the Rashomon Effect impacts (1) the existence of simple-yet-accurate models, (2) flexibility to address user preferences, such as fairness and monotonicity, without losing performance, (3) uncertainty in predictions, fairness, and explanations, (4) reliable variable importance, (5) algorithm choice, specifically, providing advanced knowledge of which algorithms might be suitable for a given problem, and (6) public policy. We also discuss a theory of when the Rashomon Effect occurs and why. Our goal is to illustrate how the Rashomon Effect can have a massive impact on the use of machine learning for complex problems in society.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity

    cs.LG 2025-05 conditional novelty 7.0 of 10

    Omitting protected attributes can cut disparate impact substantially with negligible accuracy loss, and new model-multiplicity bounds explain when fairer unaware models exist.

  2. Is Model Instability just Noise to be Tolerated or a Property that can be Managed?

    cs.SE 2026-07 accept novelty 6.0 of 10

    Performance instability in SBSE is pervasive yet partially manageable by labeling, acquisition, complexity and Gini splits; a data-inherent residual floor remains.

  3. VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performance

    cs.LG 2025-07 conditional novelty 4.0 of 10

    VAR is a visual analytics application that uses radial basis function interpolation to create heatmaps and scatter plots for horizontal comparison of models in a Rashomon set.

  4. Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices

    cs.LG 2025-06 conditional novelty 4.0 of 10

    A best-practices workflow for unsupervised scientific discovery, illustrated by a stability- and generalizability-driven clustering case study of Milky Way globular clusters using APOGEE data.

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