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In this note, I study how the precision of a binary classifier depends on the ratio r of positive to negative cases in the test set, as well as the… Click to show full abstract
In this note, I study how the precision of a binary classifier depends on the ratio r 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 r, which seems not to be well known. It also allows prediction of how Fβ and the precision gain and recall gain measures of Flach and Kull (2015) vary with r.
Keywords:
precision recall;
imbalance precision;
class imbalance;
effect class;
precision
Journal Title: Neural Computation
Year Published: 2020
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Journal Title: Neural Computation
Year Published: 2020
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!