The Effect of Class Imbalance on Precision-Recall Curves

In this note, I study how the precision of a binary classifier depends on the ratio of positive to negative cases in the test set, as well as the classifier's true and false-positive rates. This relationship allows prediction of how the precision-recall curve will change with , which seems not...

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Published inNeural computation Vol. 33; no. 4; pp. 853 - 857
Main Author Williams, Christopher K. I.
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.04.2021
MIT Press Journals, The
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Summary:In this note, I study how the precision of a binary classifier depends on the ratio of positive to negative cases in the test set, as well as the classifier's true and false-positive rates. This relationship allows prediction of how the precision-recall curve will change with , which seems not to be well known. It also allows prediction of how and the precision gain and recall gain measures of Flach and Kull (2015) vary with .
Bibliography:April, 2021
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0899-7667
1530-888X
DOI:10.1162/neco_a_01362