Sensitivity and Specificity versus Precision and Recall, and Related Dilemmas

William Cullerne Bown ()

Journal of Classification, 2024, vol. 41, issue 2, No 9, 402-426

Abstract: Abstract Many evaluations of binary classifiers begin by adopting a pair of indicators, most often sensitivity and specificity or precision and recall. Despite this, we lack a general, pan-disciplinary basis for choosing one pair over the other, or over one of four other sibling pairs. Related obscurity afflicts the choice between the receiver operating characteristic and the precision-recall curve. Here, I return to first principles to separate concerns and distinguish more than 50 foundational concepts. This allows me to establish six rules that allow one to identify which pair is correct. The choice depends on the context in which the classifier is to operate, the intended use of the classifications, their intended user(s), and the measurability of the underlying classes, but not skew. The rules can be applied by those who develop, operate, or regulate them to classifiers composed of technology, people, or combinations of the two.

Keywords: Foundations of statistics; Diagnostics; Information retrieval; Signal processing; Machine learning; Evaluation; Skew (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00357-024-09478-y

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