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

Summary: 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...

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Detalles Bibliográficos
Autores principales: Grau, Jan, Grosse, Ivo, Keilwagen, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514923/
https://www.ncbi.nlm.nih.gov/pubmed/25810428
http://dx.doi.org/10.1093/bioinformatics/btv153
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author Grau, Jan
Grosse, Ivo
Keilwagen, Jens
author_facet Grau, Jan
Grosse, Ivo
Keilwagen, Jens
author_sort Grau, Jan
collection PubMed
description Summary: 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. Availability and implementation: PRROC is available from CRAN and is licensed under GPL 3. Contact: grau@informatik.uni-halle.de
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spelling pubmed-45149232015-07-27 PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R Grau, Jan Grosse, Ivo Keilwagen, Jens Bioinformatics Applications Notes Summary: 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. Availability and implementation: PRROC is available from CRAN and is licensed under GPL 3. Contact: grau@informatik.uni-halle.de Oxford University Press 2015-08-01 2015-03-24 /pmc/articles/PMC4514923/ /pubmed/25810428 http://dx.doi.org/10.1093/bioinformatics/btv153 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Grau, Jan
Grosse, Ivo
Keilwagen, Jens
PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
title PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
title_full PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
title_fullStr PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
title_full_unstemmed PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
title_short PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
title_sort prroc: computing and visualizing precision-recall and receiver operating characteristic curves in r
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514923/
https://www.ncbi.nlm.nih.gov/pubmed/25810428
http://dx.doi.org/10.1093/bioinformatics/btv153
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