What question did this study set out to answer?

The aim is to systematically evaluate the quality of calibration metrics used in uncertainty quantification for regression models.

April 19, 2026International Journal of Approximate ReasoningOpen Access

Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models

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Authors

JWJelke WibbekeCarl von Ossietzky Universität Oldenburg NSNico SchönfischCarl von Ossietzky Universität Oldenburg SRSebastian RohjansGeoInformation (United Kingdom)

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Implication

This systematic benchmark evaluates calibration metrics to enhance uncertainty quantification in data-driven models, indicating implications for calibration research.

Key Points

The aim is to systematically evaluate the quality of calibration metrics used in uncertainty quantification for regression models.
Extracted and categorized regression calibration metrics from existing literature.
Conducted controlled experiments using real-world, synthetic, and artificially miscalibrated data.
Analyzed the consistency and reliability of various calibration metrics.
Calibration metrics often yielded conflicting evaluations of the same recalibration results.
Expected Normalized Calibration Error (ENCE) and Coverage Width-based Criterion (CWC) were identified as the most reliable metrics.
Reliable evaluation requires at least 500-1000 samples for robust results in recalibration studies.

Cite This Study

Wibbeke et al. (2026) studied this question.

synapsesocial.com/papers/69e4702d010ef96374d8d69a https://doi.org/10.1016/j.ijar.2026.109685

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Implication

This systematic benchmark evaluates calibration metrics to enhance uncertainty quantification in data-driven models, indicating implications for calibration research.

Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models

Discussion

Member takes

Implication

Key Points

Cite This Study

Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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