Summary of Investigating the Impact Of Balancing, Filtering, and Complexity on Predictive Multiplicity: a Data-centric Perspective, by Mustafa Cavus and Przemyslaw Biecek
Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective
by Mustafa Cavus, Przemyslaw Biecek
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The proposed research addresses the Rashomon effect in model selection, where multiple models achieve similar accuracy but produce different predictions due to predictive multiplicity. Traditional methods prioritize accuracy over addressing this issue, leading to arbitrary model outcomes with serious consequences. Data-centric approaches can mitigate these problems by optimizing data preprocessing techniques, but recent studies suggest that these methods may inadvertently inflate predictive multiplicity. This study investigates how various balancing and filtering methods impact predictive multiplicity and model stability on 21 real-world datasets, applying different techniques to assess the level of introduced multiplicity using the Rashomon effect. The findings provide insights into the relationship between balancing methods, data complexity, and predictive multiplicity, demonstrating how data-centric AI strategies can improve model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a problem called the Rashomon effect in machine learning. Imagine you have many models that are good at making predictions, but they all give different answers to the same question. This makes it hard to choose which model to use because none of them seem better than the others. The authors look at how different ways of preparing data affect this problem. They test 21 real datasets using different techniques and see if these methods make the problem worse or better. The results can help us build better machine learning models. |
Keywords
» Artificial intelligence » Machine learning
