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[2602.12975] Extending confidence calibration to generalised measures of variation
Summary
The paper introduces the Variation Calibration Error (VCE) metric, extending confidence calibration methods in machine learning to assess the calibration of various metrics of variation, demonstrating its effectiveness through numerical examples.
Why It Matters
This research is significant as it enhances the understanding of confidence calibration in machine learning models, which is crucial for improving model reliability and performance. By introducing the VCE metric, the authors provide a new tool for practitioners to better evaluate and refine their models, potentially leading to more accurate predictions in various applications.
Key Takeaways
- The Variation Calibration Error (VCE) metric is proposed for assessing calibration in machine learning classifiers.
- VCE extends the Expected Calibration Error (ECE) to evaluate any metric of variation, not just confidence.
- Numerical examples demonstrate that VCE approaches zero with increased data samples, indicating effective calibration.
- The paper contrasts VCE with other calibration metrics, highlighting its advantages in specific scenarios.
- Improved calibration metrics can enhance the reliability of machine learning predictions across various fields.
Computer Science > Machine Learning arXiv:2602.12975 (cs) [Submitted on 13 Feb 2026] Title:Extending confidence calibration to generalised measures of variation Authors:Andrew Thompson, Vivek Desai View a PDF of the paper titled Extending confidence calibration to generalised measures of variation, by Andrew Thompson and 1 other authors View PDF HTML (experimental) Abstract:We propose the Variation Calibration Error (VCE) metric for assessing the calibration of machine learning classifiers. The metric can be viewed as an extension of the well-known Expected Calibration Error (ECE) which assesses the calibration of the maximum probability or confidence. Other ways of measuring the variation of a probability distribution exist which have the advantage of taking into account the full probability distribution, for example the Shannon entropy. We show how the ECE approach can be extended from assessing confidence calibration to assessing the calibration of any metric of variation. We present numerical examples upon synthetic predictions which are perfectly calibrated by design, demonstrating that, in this scenario, the VCE has the desired property of approaching zero as the number of data samples increases, in contrast to another entropy-based calibration metric (the UCE) which has been proposed in the literature. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.12975 [cs.LG] (or arXiv:2602.12975v1 [cs.LG] for this version) https://doi...
