![[2602.18573] Multiclass Calibration Assessment and Recalibration of Probability Predictions via the Linear Log Odds Calibration Function](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18573] Multiclass Calibration Assessment and Recalibration of Probability Predictions via the Linear Log Odds Calibration Function
Summary
The paper presents a novel method for assessing and recalibrating probability predictions in multiclass classification tasks, addressing limitations of existing techniques.
Why It Matters
Accurate probability predictions are crucial in machine learning applications. This study introduces a method that enhances model calibration without requiring internal access, making it applicable across various domains. Improved calibration can lead to better decision-making in critical areas like healthcare and environmental science.
Key Takeaways
- Introduces Multicategory Linear Log Odds (MCLLO) recalibration method.
- MCLLO assesses calibration without needing internal model access.
- The method is interpretable and applicable to various classification problems.
- Demonstrated effectiveness through simulations and real-world case studies.
- Compares favorably against existing recalibration techniques.
Statistics > Machine Learning arXiv:2602.18573 (stat) [Submitted on 20 Feb 2026] Title:Multiclass Calibration Assessment and Recalibration of Probability Predictions via the Linear Log Odds Calibration Function Authors:Amy Vennos, Xin Xing, Christopher T. Franck View a PDF of the paper titled Multiclass Calibration Assessment and Recalibration of Probability Predictions via the Linear Log Odds Calibration Function, by Amy Vennos and 2 other authors View PDF HTML (experimental) Abstract:Machine-generated probability predictions are essential in modern classification tasks such as image classification. A model is well calibrated when its predicted probabilities correspond to observed event frequencies. Despite the need for multicategory recalibration methods, existing methods are limited to (i) comparing calibration between two or more models rather than directly assessing the calibration of a single model, (ii) requiring under-the-hood model access, e.g., accessing logit-scale predictions within the layers of a neural network, and (iii) providing output which is difficult for human analysts to understand. To overcome (i)-(iii), we propose Multicategory Linear Log Odds (MCLLO) recalibration, which (i) includes a likelihood ratio hypothesis test to assess calibration, (ii) does not require under-the-hood access to models and is thus applicable on a wide range of classification problems, and (iii) can be easily interpreted. We demonstrate the effectiveness of the MCLLO method ...
