Summary of Imbalance in Regression Datasets, by Daniel Kowatsch et al.
Imbalance in Regression Datasets
by Daniel Kowatsch, Nicolas M. Müller, Kilian Tscharke, Philip Sperl, Konstantin Bötinger
First submitted to arxiv on: 19 Feb 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 The paper investigates the issue of class imbalance in regression tasks, arguing that it is an equally important problem as in classification. The authors demonstrate that due to under- or over-representations in a dataset’s target distribution, regressors tend to degenerate into naive models, neglecting uncommon training data and over-representing frequently seen targets. By analyzing this problem theoretically, the researchers develop a definition of imbalance in regression, which generalizes commonly used measures for classification. The paper aims to raise awareness about the overlooked issue of imbalance in regression and provide common ground for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how some problems in machine learning are not just limited to image recognition or text analysis but can also affect other areas like predicting continuous values. It shows that even when trying to make predictions, imbalances in the data can cause models to become really simple and ignore important information. The researchers come up with a way to define this problem and hope it will help others study this issue further. |
Keywords
* Artificial intelligence * Classification * Machine learning * Regression
