Probabilistic Conformal Coverage Guarantees in Small-Data Settings
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911163224162304 |
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| author | Zwart, Petrus H. |
| author_facet | Zwart, Petrus H. |
| contents | Conformal prediction provides distribution-free prediction sets with guaranteed marginal coverage. However, in split conformal prediction this guarantee is training-conditional only in expectation: across many calibration draws, the average coverage equals the nominal level, but the realized coverage for a single calibration set may vary substantially. This variance undermines effective risk control in practical applications. Here we introduce the Small Sample Beta Correction (SSBC), a plug-and-play adjustment to the conformal significance level that leverages the exact finite-sample distribution of conformal coverage to provide probabilistic guarantees, ensuring that with user-defined probability over the calibration draw, the deployed predictor achieves at least the desired coverage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15349 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Conformal Coverage Guarantees in Small-Data Settings Zwart, Petrus H. Machine Learning Conformal prediction provides distribution-free prediction sets with guaranteed marginal coverage. However, in split conformal prediction this guarantee is training-conditional only in expectation: across many calibration draws, the average coverage equals the nominal level, but the realized coverage for a single calibration set may vary substantially. This variance undermines effective risk control in practical applications. Here we introduce the Small Sample Beta Correction (SSBC), a plug-and-play adjustment to the conformal significance level that leverages the exact finite-sample distribution of conformal coverage to provide probabilistic guarantees, ensuring that with user-defined probability over the calibration draw, the deployed predictor achieves at least the desired coverage. |
| title | Probabilistic Conformal Coverage Guarantees in Small-Data Settings |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.15349 |