Inductive Venn-Abers and related regressors
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917469316186112 |
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| author | Petej, Ivan Vovk, Vladimir |
| author_facet | Petej, Ivan Vovk, Vladimir |
| contents | Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary classification, with a recent extension to bounded regression. We generalize them to the case of unbounded regression, which requires adding an element of conformal prediction. In our simulation and empirical studies we investigate the predictive efficiency of point regressors derived from Venn-Abers regressors and argue that they somewhat improve the predictive efficiency of standard regressors for larger training sets. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_06646 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inductive Venn-Abers and related regressors Petej, Ivan Vovk, Vladimir Machine Learning 68Q32 (Primary) 62G08, 68T05 (Secondary) I.2.6 Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary classification, with a recent extension to bounded regression. We generalize them to the case of unbounded regression, which requires adding an element of conformal prediction. In our simulation and empirical studies we investigate the predictive efficiency of point regressors derived from Venn-Abers regressors and argue that they somewhat improve the predictive efficiency of standard regressors for larger training sets. |
| title | Inductive Venn-Abers and related regressors |
| topic | Machine Learning 68Q32 (Primary) 62G08, 68T05 (Secondary) I.2.6 |
| url | https://arxiv.org/abs/2605.06646 |