Calibration of Machine Learning Models
Abstract
The evaluation of machine learning models is a crucial step before their application because it is essential to assess how well a model will behave for every single case. In many real applications, not only is it important to know the “total” or the “average” error of the model, it is also important to know how this error is distributed and how well confidence or probability estimations are made. Many current machine learning techniques are good in overall results but have a bad distribution assessment of the error. For these cases, calibration techniques have been developed as postprocessing techniques in order to improve the probability estimation or the error distribution of an existing model. This chapter presents the most common calibration techniques and calibration measures. Both classification and regression are covered, and a taxonomy of calibration techniques is established. Special attention is given to probabilistic classifier calibration.
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Most studies did not evaluate the algorithm's performance across different demographic groups or use calibration and fairness metrics to assess disparities in predictions across these groups.
“Most studies did not evaluate the algorithm’s performance across different demographic groups or use calibration and fairness (e.g., demographic parity, equal opportunity) metrics to assess disparities in predictions across these groups12,13.”
From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [13]
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Cited by 59
- Authors
- Bella, A., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J.
- Affiliations
- Universitat Politècnica de València (ES)
- Universidad Politécnica de Valencia, Spain
- ORCID
- Ferri, Cèsar
- Venue
- Handbook of Research on Machine Learning Applications and Trends
- Publisher
- IGI Global
- Published
- 2010
- Pages
- 128-146
- OpenAlex
- W4232704105
- Cited by
- 59
- References
- 0
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 11 · 2023: 11 · 2022: 9 · 2021: 4 · 2020: 5 · 2019: 2 · 2018: 1 · 2017: 1 · 2014: 1 · 2013: 2 · 2012: 2
- FWCI
- 2.25
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- IGI Global eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- model calibration, probability estimation, error distribution, probabilistic classifiers, post-processing, classification, regression analysis
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