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

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dc.contributor.author Santos Costa, Vitor
dc.contributor.author Page, David
dc.contributor.author Davis, Jesse
dc.contributor.author Boyd, Kendrick
dc.date.accessioned 2012-07-12T17:35:08Z
dc.date.available 2012-07-12T17:35:08Z
dc.date.issued 2012-05-30
dc.identifier.citation TR1772 en
dc.identifier.uri http://digital.library.wisc.edu/1793/61736
dc.description.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. en
dc.description.provenance Submitted by Jody Hoesly (jhoesly@wisc.edu) on 2012-07-12T17:35:08Z No. of bitstreams: 1 TR1772 prspace_techreport.pdf: 418934 bytes, checksum: 41090e9a1c6fc0d39d54a4c996a11839 (MD5) en
dc.description.provenance Made available in DSpace on 2012-07-12T17:35:08Z (GMT). No. of bitstreams: 1 TR1772 prspace_techreport.pdf: 418934 bytes, checksum: 41090e9a1c6fc0d39d54a4c996a11839 (MD5) Previous issue date: 2012-05-30 en
dc.publisher University of Wisconsin-Madison Department of Computer Sciences en
dc.subject F1 score en
dc.subject precision-recall curves en
dc.title Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation en
dc.type Technical Report en
dc.contributor.affiliation University of Wisconsin-Madison Department of Computer Sciences

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