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arXiv · 2106.00858

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals

Jiri Navratil·Benjamin Elder·Matthew Arnold·Soumya Ghosh·Prasanna Sattigeri
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Abstract

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to a specific operating point, making evaluation and comparison across different studies difficult. Our work leverages: (1) the concept of operating characteristics curves and (2) the notion of a gain over a simple reference, to derive a novel operating point agnostic assessment methodology for prediction intervals. The paper describes the corresponding algorithm, provides a theoretical analysis, and demonstrates its utility in multiple scenarios. We argue that the proposed method addresses the current need for comprehensive assessment of prediction intervals and thus represents a valuable addition to the uncertainty quantification toolbox.

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Jiri Navratil, Benjamin Elder, Matthew Arnold, Soumya Ghosh, Prasanna Sattigeri. 2021-06-01. Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals. https://arxiv.org/abs/2106.00858

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