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

被引：257

作者：

Grau, Jan ^{[1
,2
]}

Grosse, Ivo ^{[1
,2
,3
]}

Keilwagen, Jens ^{[4
]}

机构：

[1] Univ Halle Wittenberg, Inst Comp Sci, D-06108 Halle, Saale, Germany

[2] Univ Halle Wittenberg, Univ Zentrum Informat, D-06108 Halle, Saale, Germany

[3] German Ctr Integrat Biodivers Res iDiv, Leipzig, Germany

[4] Fed Res Ctr Cultivated Plants, JKI, Inst Biosafety Plant Biotechnol, Quedlinburg, Germany

来源：

BIOINFORMATICS | 2015年 / 31卷 / 15期

关键词：

D O I：

10.1093/bioinformatics/btv153

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：2595 / 2597

页数：3

共 8 条

[1]

[Anonymous], 2006, P 23 INT C MACHINE L, DOI DOI 10.1145/1143844.1143874

[2]

Boyd Kendrick, 2013, Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2013. Proceedings: LNCS 8190, P451, DOI 10.1007/978-3-642-40994-3_29

[3]

Davis J, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P677

[4] A general approach for discriminative de novo motif discovery from high-throughput data [J].