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A power-controlled reliability assessment for multi-class probabilistic classifiers

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  • Hyukjun Gweon

    (Western University)

Abstract

In multi-class classification, the output of a probabilistic classifier is a probability distribution of the classes. In this work, we focus on a statistical assessment of the reliability of probabilistic classifiers for multi-class problems. Our approach generates a Pearson $$\chi ^2$$ χ 2 statistic based on the k-nearest-neighbors in the prediction space. Further, we develop a Bayesian approach for estimating the expected power of the reliability test that can be used for an appropriate sample size k. We propose a sampling algorithm and demonstrate that this algorithm obtains a valid prior distribution. The effectiveness of the proposed reliability test and expected power is evaluated through a simulation study. We also provide illustrative examples of the proposed methods with practical applications.

Suggested Citation

  • Hyukjun Gweon, 2023. "A power-controlled reliability assessment for multi-class probabilistic classifiers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(4), pages 927-949, December.
    • Handle: RePEc:spr:advdac:v:17:y:2023:i:4:d:10.1007_s11634-022-00528-0
    DOI: 10.1007/s11634-022-00528-0
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    3. Allan H. Murphy & Robert L. Winkler, 1977. "Reliability of Subjective Probability Forecasts of Precipitation and Temperature," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(1), pages 41-47, March.
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