Summary of Target Strangeness: a Novel Conformal Prediction Difficulty Estimator, by Alexis Bose et al.
Target Strangeness: A Novel Conformal Prediction Difficulty Estimator
by Alexis Bose, Jonathan Ethier, Paul Guinand
First submitted to arxiv on: 24 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 This paper presents Target Strangeness, a new approach for estimating prediction interval difficulties in conformal prediction. By analyzing how unusual a prediction is compared to its neighboring targets, Target Strangeness outperforms current benchmarks. The method is tested and compared with others in the context of several regression experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Target Strangeness is a new way to measure how hard it is to make predictions in certain situations. It looks at how unusual a prediction is compared to what’s likely to happen nearby, and does better than other methods in doing this. The paper shows that Target Strangeness works well in several different prediction tasks. |
Keywords
» Artificial intelligence » Regression
