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[2508.11460] Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models
Summary
This article evaluates uncertainty estimates in binary classification models, comparing six probabilistic machine learning algorithms to assess their calibration and performance on synthetic datasets.
Why It Matters
Understanding uncertainty in machine learning models is crucial for scientific applications, as it impacts decision-making and the reliability of predictions. This study highlights the limitations of current algorithms in accurately reflecting uncertainty, which is vital for researchers in data-driven fields.
Key Takeaways
- The study investigates six probabilistic machine learning algorithms for uncertainty estimation.
- All algorithms showed reasonable calibration but struggled with out-of-distribution data.
- The findings emphasize the need for improved methods in uncertainty quantification.
Computer Science > Machine Learning arXiv:2508.11460 (cs) [Submitted on 15 Aug 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models Authors:Aurora Grefsrud, Nello Blaser, Trygve Buanes View a PDF of the paper titled Calibrated and uncertain? Evaluating uncertainty estimates in binary classification models, by Aurora Grefsrud and 1 other authors View PDF HTML (experimental) Abstract:Rigorous statistical methods, including parameter estimation with accompanying uncertainties, underpin the validity of scientific discovery, especially in the natural sciences. With increasingly complex data models such as deep learning techniques, uncertainty quantification has become exceedingly difficult and a plethora of techniques have been proposed. In this case study, we use the unifying framework of approximate Bayesian inference combined with empirical tests on carefully created synthetic classification datasets to investigate qualitative properties of six different probabilistic machine learning algorithms for class probability and uncertainty estimation: (i) a neural network ensemble, (ii) neural network ensemble with conflictual loss, (iii) evidential deep learning, (iv) a single neural network with Monte Carlo Dropout, (v) Gaussian process classification and (vi) a Dirichlet process mixture model. We check if the algorithms produce uncertainty estimates which reflect commonly desired ...
