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

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

Jan Grau  1 , Ivo Grosse  2 , Jens Keilwagen  3
Affiliations

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

Jan Grau et al. Bioinformatics. .

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.

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Figures

Fig. 1.
Fig. 1.
Plots of ROC (left) and PR (right) curves generated by PRROC. For the ROC curve, we consider hard-labeled data and show the plotting variant with a color scale that indicates classification thresholds yielding the points on the curve. For the PR curve, we consider soft-labeled data and show a comparative plot for two classifiers as solid and dashed lines. We also include the maximal and minimal possible curves and the curve of a random classifier for the given soft-labels
See this image and copyright information in PMC

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