Summary of Amazing Things Come From Having Many Good Models, by Cynthia Rudin et al.
Amazing Things Come From Having Many Good Models
by Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This perspective piece proposes reshaping the way we think about machine learning, particularly for tabular data problems in the nondeterministic (noisy) setting. The Rashomon Effect, coined by Leo Breiman, describes the phenomenon where many equally good predictive models exist for the same dataset. This phenomenon sparks both magic and consternation, but mostly magic. The paper addresses how the Rashomon Effect impacts simple-yet-accurate models, flexibility to address user preferences, uncertainty in predictions, reliable variable importance, algorithm choice, and public policy. A theory is also discussed on when the Rashomon Effect occurs and why. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning has a phenomenon called the Rashomon Effect, which means there are many good predictive models for the same data. This paper thinks about how this effect changes how we use machine learning to solve real-world problems. It talks about how it affects simple-yet-accurate models, making choices based on user preferences, and understanding what makes predictions uncertain. |
Keywords
» Artificial intelligence » Machine learning
