Measuring Classification Decision Certainty and Doubt

Alexander M. Berenbeim, Iain J. Cruickshank, Susmit Jha, Robert H. Thomson, Nathaniel D. Bastian
3/25/2023
stat.MLcs.AIcs.LG

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Abstract

Quantitative characterizations and estimations of uncertainty are of fundamental importance in optimization and decision-making processes. Herein, we propose intuitive scores, which we call certainty and doubt, that can be used in both a Bayesian and frequentist framework to assess and compare the quality and uncertainty of predictions in (multi-)classification decision machine learning problems.

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Cite this paper

@article{berenbeim2023measuring,
  title  = {Measuring Classification Decision Certainty and Doubt},
  author = {Alexander M. Berenbeim and Iain J. Cruickshank and Susmit Jha and Robert H. Thomson and Nathaniel D. Bastian},
  year   = {2023},
  eprint = {2303.14568},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.14568v2}
}

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