Target Strangeness: A Novel Conformal Prediction Difficulty Estimator
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914988237520896 |
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| author | Bose, Alexis Ethier, Jonathan Guinand, Paul |
| author_facet | Bose, Alexis Ethier, Jonathan Guinand, Paul |
| contents | This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs). By assessing how atypical a prediction is within the context of its nearest neighbours' target distribution, Target Strangeness can surpass the current state-of-the-art performance. This novel difficulty estimator is evaluated against others in the context of several conformal regression experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Target Strangeness: A Novel Conformal Prediction Difficulty Estimator Bose, Alexis Ethier, Jonathan Guinand, Paul Machine Learning This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs). By assessing how atypical a prediction is within the context of its nearest neighbours' target distribution, Target Strangeness can surpass the current state-of-the-art performance. This novel difficulty estimator is evaluated against others in the context of several conformal regression experiments. |
| title | Target Strangeness: A Novel Conformal Prediction Difficulty Estimator |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.19077 |