Measuring Classification Decision Certainty and Doubt

• URL: http://arxiv.org/abs/2303.14568v2
• Date: Tue, 28 Mar 2023 01:27:51 GMT
• Title: Measuring Classification Decision Certainty and Doubt
• Authors: Alexander M. Berenbeim, Iain J. Cruickshank, Susmit Jha, Robert H. Thomson, and Nathaniel D. Bastian
• Abstract summary: We propose intuitive scores, which we call certainty and doubt, to assess and compare the quality and uncertainty of predictions in (multi-)classification decision machine learning problems.
• Score: 61.13511467941388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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