Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915681996374016 |
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| author | Detavernier, Adrián De Bock, Jasper |
| author_facet | Detavernier, Adrián De Bock, Jasper |
| contents | We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We compare both approaches on a number of benchmark datasets and show that there is no clear winner between the two, but that they are complementary and can be combined to obtain a hybrid approach that outperforms both RQ and UQ. As a byproduct of our approach, for each dataset, we also obtain an assessment of the relative importance of uncertainty and robustness as sources of unreliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier Detavernier, Adrián De Bock, Jasper Machine Learning We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We compare both approaches on a number of benchmark datasets and show that there is no clear winner between the two, but that they are complementary and can be combined to obtain a hybrid approach that outperforms both RQ and UQ. As a byproduct of our approach, for each dataset, we also obtain an assessment of the relative importance of uncertainty and robustness as sources of unreliability. |
| title | Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.15492 |