Grau Jan, Grosse Ivo, Keilwagen Jens
Institute of Computer Science and Universitätszentrum Informatik, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
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.
Bioinformatics. 2015 Aug 1;31(15):2595-7. doi: 10.1093/bioinformatics/btv153. Epub 2015 Mar 24.
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.
精确率-召回率（PR）曲线和受试者工作特征（ROC）曲线是评估分类器性能的重要指标。在此，我们展示了R包PRROC，它能够计算并可视化PR曲线和ROC曲线。与现有的R包不同，PRROC可以通过在PR曲线各点之间进行连续插值，来计算软标签数据的PR曲线、ROC曲线以及这些曲线下的面积。此外，PRROC还提供了一个通用绘图函数，用于生成具有发表质量的PR曲线和ROC曲线图形。
