A Unified Evaluation Framework for Epistemic Predictions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866929715684573184 |
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| author | Manchingal, Shireen Kudukkil Mubashar, Muhammad Wang, Kaizheng Cuzzolin, Fabio |
| author_facet | Manchingal, Shireen Kudukkil Mubashar, Muhammad Wang, Kaizheng Cuzzolin, Fabio |
| contents | Predictions of uncertainty-aware models are diverse, ranging from single point estimates (often averaged over prediction samples) to predictive distributions, to set-valued or credal-set representations. We propose a novel unified evaluation framework for uncertainty-aware classifiers, applicable to a wide range of model classes, which allows users to tailor the trade-off between accuracy and precision of predictions via a suitably designed performance metric. This makes possible the selection of the most suitable model for a particular real-world application as a function of the desired trade-off. Our experiments, concerning Bayesian, ensemble, evidential, deterministic, credal and belief function classifiers on the CIFAR-10, MNIST and CIFAR-100 datasets, show that the metric behaves as desired. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified Evaluation Framework for Epistemic Predictions Manchingal, Shireen Kudukkil Mubashar, Muhammad Wang, Kaizheng Cuzzolin, Fabio Machine Learning Predictions of uncertainty-aware models are diverse, ranging from single point estimates (often averaged over prediction samples) to predictive distributions, to set-valued or credal-set representations. We propose a novel unified evaluation framework for uncertainty-aware classifiers, applicable to a wide range of model classes, which allows users to tailor the trade-off between accuracy and precision of predictions via a suitably designed performance metric. This makes possible the selection of the most suitable model for a particular real-world application as a function of the desired trade-off. Our experiments, concerning Bayesian, ensemble, evidential, deterministic, credal and belief function classifiers on the CIFAR-10, MNIST and CIFAR-100 datasets, show that the metric behaves as desired. |
| title | A Unified Evaluation Framework for Epistemic Predictions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.16912 |