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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.13872 |
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| _version_ | 1866908867729817600 |
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| author | Ciosek, Kamil Felicioni, Nicolò Ghiassian, Sina Litwin, Juan Elenter Tonolini, Francesco Gustafsson, David Garcia-Martin, Eva Gonzalez, Carmen Barcena Bertrand-Lalo, Raphaëlle |
| author_facet | Ciosek, Kamil Felicioni, Nicolò Ghiassian, Sina Litwin, Juan Elenter Tonolini, Francesco Gustafsson, David Garcia-Martin, Eva Gonzalez, Carmen Barcena Bertrand-Lalo, Raphaëlle |
| contents | We make two contributions to the problem of estimating the $L_1$ calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation. Second, we provide a method of modifying any classifier so that its calibration error can be upper bounded efficiently without significantly impacting classifier performance and without any restrictive assumptions. All our results are non-asymptotic and distribution-free. We conclude by providing advice on how to measure calibration error in practice. Our methods yield practical procedures that can be run on real-world datasets with modest overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Measuring Uncertainty Calibration Ciosek, Kamil Felicioni, Nicolò Ghiassian, Sina Litwin, Juan Elenter Tonolini, Francesco Gustafsson, David Garcia-Martin, Eva Gonzalez, Carmen Barcena Bertrand-Lalo, Raphaëlle Machine Learning We make two contributions to the problem of estimating the $L_1$ calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation. Second, we provide a method of modifying any classifier so that its calibration error can be upper bounded efficiently without significantly impacting classifier performance and without any restrictive assumptions. All our results are non-asymptotic and distribution-free. We conclude by providing advice on how to measure calibration error in practice. Our methods yield practical procedures that can be run on real-world datasets with modest overhead. |
| title | Measuring Uncertainty Calibration |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.13872 |