PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R

Jan Grau; Ivo Grosse; Jens Keilwagen

doi:10.1093/bioinformatics/btv153

PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R

Bioinformatics. 2015 Aug 1;31(15):2595-7. doi: 10.1093/bioinformatics/btv153. Epub 2015 Mar 24.

Authors

Jan Grau¹, Ivo Grosse², Jens Keilwagen³

Affiliations

¹ Institute of Computer Science and Universitätszentrum Informatik, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
² Institute of Computer Science and Universitätszentrum Informatik, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany and.
³ Institute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, Quedlinburg, Germany.

Abstract

Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance. Here, we present the R-package PRROC, which allows for computing and visualizing both PR and ROC curves. In contrast to available R-packages, PRROC allows for computing PR and ROC curves and areas under these curves for soft-labeled data using a continuous interpolation between the points of PR curves. In addition, PRROC provides a generic plot function for generating publication-quality graphics of PR and ROC curves.

MeSH terms

Area Under Curve
Computer Graphics*
Data Interpretation, Statistical*
Humans
Mathematical Computing*
Pattern Recognition, Automated
ROC Curve*
Software*
User-Computer Interface