Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

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Author(s): Santos Costa, Vitor; Page, David; Davis, Jesse; Boyd, Kendrick
Publisher: University of Wisconsin-Madison Department of Computer Sciences
Citation: TR1772
Date: May 30, 2012
Subject(s): F1 score; precision-recall curves
Abstract: Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning.
Permanent link: http://digital.library.wisc.edu/1793/61736
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