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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.13843 |
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| _version_ | 1866910307746578432 |
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| author | Fazekas, Attila Kovacs, Gyorgy |
| author_facet | Fazekas, Attila Kovacs, Gyorgy |
| contents | K-fold cross-validation is a widely used tool for assessing classifier performance. The reproducibility crisis faced by artificial intelligence partly results from the irreproducibility of reported k-fold cross-validation-based performance scores. Recently, we introduced numerical techniques to test the consistency of claimed performance scores and experimental setups. In a crucial use case, the method relies on the combinatorial enumeration of all k-fold configurations, for which we proposed an algorithm in the binary classification case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enumerating the k-fold configurations in multi-class classification problems Fazekas, Attila Kovacs, Gyorgy Machine Learning K-fold cross-validation is a widely used tool for assessing classifier performance. The reproducibility crisis faced by artificial intelligence partly results from the irreproducibility of reported k-fold cross-validation-based performance scores. Recently, we introduced numerical techniques to test the consistency of claimed performance scores and experimental setups. In a crucial use case, the method relies on the combinatorial enumeration of all k-fold configurations, for which we proposed an algorithm in the binary classification case. |
| title | Enumerating the k-fold configurations in multi-class classification problems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.13843 |