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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.11267 |
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| _version_ | 1866914036327645184 |
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| author | Petej, Ivan |
| author_facet | Petej, Ivan |
| contents | This paper introduces a new Python package specifically designed to address calibration of probabilistic classifiers under dataset shift. The method is demonstrated in binary and multi-class settings and its effectiveness is measured against a number of existing post-hoc calibration methods. The empirical results are promising and suggest that our technique can be helpful in a variety of settings for batch and online learning classification problems where the underlying data distribution changes between the training and test sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Protected Probabilistic Classification Library Petej, Ivan Machine Learning This paper introduces a new Python package specifically designed to address calibration of probabilistic classifiers under dataset shift. The method is demonstrated in binary and multi-class settings and its effectiveness is measured against a number of existing post-hoc calibration methods. The empirical results are promising and suggest that our technique can be helpful in a variety of settings for batch and online learning classification problems where the underlying data distribution changes between the training and test sets. |
| title | Protected Probabilistic Classification Library |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.11267 |