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arXiv · 2501.16912

A Unified Evaluation Framework for Epistemic Predictions

Shireen Kudukkil Manchingal·Muhammad Mubashar·Kaizheng Wang·Fabio Cuzzolin
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Abstract

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.

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Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang, Fabio Cuzzolin. 2025-01-28. A Unified Evaluation Framework for Epistemic Predictions. https://arxiv.org/abs/2501.16912

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