June 6, 2024Open Access

Reassessing How to Compare and Improve the Calibration of Machine Learning Models

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Authors

MCMuthu ChidambaramAll India Institute of Medical Sciences Bhopal RGRong GeHunan University of Science and Technology

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Implication

Theoretical analysis reveals trivial recalibration can falsely appear state-of-the-art in machine learning models, highlighting the need for joint generalization metrics.

Key Points

Trivial recalibration approaches can mimic state-of-the-art model performance unless calibration metrics are evaluated alongside negative log-likelihood.
A novel decomposition of Bregman divergences enables principled metric selection based on generalization error and effectively exposes trivial calibration.
Extended reliability diagrams allow joint visualization of model calibration and estimated generalization error, improving evaluation across machine learning domains.

Cite This Study

Chidambaram et al. (2024) studied this question.

synapsesocial.com/papers/68e65e3eb6db6435875ecebe https://doi.org/10.48550/arxiv.2406.04068

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Implication

Theoretical analysis reveals trivial recalibration can falsely appear state-of-the-art in machine learning models, highlighting the need for joint generalization metrics.

Reassessing How to Compare and Improve the Calibration of Machine Learning Models

Discussion

Member takes

Implication

Key Points

Cite This Study

Reassessing How to Compare and Improve the Calibration of Machine Learning Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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