Summary of Inductive Conformal Prediction Under Data Scarcity: Exploring the Impacts Of Nonconformity Measures, by Yuko Kato et al.
Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures
by Yuko Kato, David M.J. Tax, Marco Loog
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 evaluates various nonconformity measures used in conformal prediction for uncertainty quantification. It compares absolute error-based, normalized absolute error-based, and quantile-based measures on synthetic and real-world data, considering factors like dataset size, noise, and dimensionality. The results show that no single measure consistently outperforms the others, as each measure’s effectiveness depends on the specific data characteristics. This study highlights the importance of carefully selecting nonconformity measures for different applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make predictions more accurate by using special measures called “nonconformity measures”. It tries out different types of these measures and sees how well they work with small datasets, which are common in many real-world situations. The results show that each measure works best with certain kinds of data, but none is clearly the best overall. This means we need to choose the right measure for each specific problem. |
