https://www.jstacs.de/index.php?title=AUC-PR&feed=atom&action=historyAUC-PR - Revision history2024-03-28T19:56:03ZRevision history for this page on the wikiMediaWiki 1.38.2https://www.jstacs.de/index.php?title=AUC-PR&diff=625&oldid=prevGrau: Created page with "== Area under ROC and PR curves for weighted and unweighted data == by Jens Keilwagen, Ivo Grosse, and Jan Grau Precision-recall and ROC curves are highly informative about the ..."2013-04-22T19:55:52Z<p>Created page with "== Area under ROC and PR curves for weighted and unweighted data == by Jens Keilwagen, Ivo Grosse, and Jan Grau Precision-recall and ROC curves are highly informative about the ..."</p>
<p><b>New page</b></p><div>== Area under ROC and PR curves for weighted and unweighted data ==<br />
by Jens Keilwagen, Ivo Grosse, and Jan Grau<br />
<br />
Precision-recall and ROC curves are highly informative about the performance of binary classifiers, and the area under these curves is a popular scalar performance measure for comparing different classifiers.<br />
For many applications, class labels are not provided with absolute certainty, but with some degree of confidence, often reflected by weights or soft labels assigned to the data points.<br />
Here, we provide a command line program that uses an interpolation for precision-recall curves (and ROC curves) that can also be used for weighted test data.<br />
<br />
=== Download ===<br />
After downloading [http://www.jstacs.de/download.php?which=AUC AUC.jar], you can compute the area under the precision-recall and ROC curve from lists of scores provided in one (weighted data) or two (unweighted data) files.<br />
<br />
For unweighted data, please use:<br />
java -jar AUC.jar <fg> <bg><br />
where <fg> and <bg> are files with one classification score per line for the positive (fg) and negative (bg) class, respectively.<br />
<br />
For weighted data please use:<br />
java -jar AUC.jar <weighted><br />
where <weighted> is a tab-delimited file with one classification score and the weights for fg (positive class) and bg (negative class) per line.</div>Grau