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visualizing classifier performance in R, with only 3 commands

Please support our work by citing the ROCR article in your publications:

Sing T, Sander O, Beerenwinkel N, Lengauer T. [2005] ROCR: visualizing classifier performance in R. Bioinformatics 21(20):3940-1.

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ROCR was originally developed at the Max Planck Institute for Informatics


ROCR (with obvious pronounciation) is an R package for evaluating and visualizing classifier performance. It is...

Performance measures that ROCR knows:

Accuracy, error rate, true positive rate, false positive rate, true negative rate, false negative rate, sensitivity, specificity, recall, positive predictive value, negative predictive value, precision, fallout, miss, phi correlation coefficient, Matthews correlation coefficient, mutual information, chi square statistic, odds ratio, lift value, precision/recall F measure, ROC convex hull, area under the ROC curve, precision/recall break-even point, calibration error, mean cross-entropy, root mean squared error, SAR measure, expected cost, explicit cost.

ROCR features:

ROC curves, precision/recall plots, lift charts, cost curves, custom curves by freely selecting one performance measure for the x axis and one for the y axis, handling of data from cross-validation or bootstrapping, curve averaging (vertically, horizontally, or by threshold), standard error bars, box plots, curves that are color-coded by cutoff, printing threshold values on the curve, tight integration with Rs plotting facilities (making it easy to adjust plots or to combine multiple plots), fully customizable, easy to use (only 3 commands).

Installation of ROCR

The most straightforward way to install and use ROCR is to install it from CRAN by starting R and using the install.packages function:


Alternatively you can install it from command line using the tar ball like this:


Getting started

from withing R ...



Using ROCR's 3 commands to produce a simple ROC plot:

pred <- prediction(predictions, labels)
perf <- performance(pred, measure = "tpr", x.measure = "fpr")
plot(perf, col=rainbow(10))



Questions, comments, and suggestions are very welcome. Open an issue on GitHub and we can discuss. We are also interested in seeing how ROCR is used in publications. Thus, if you have prepared a paper using ROCR we'd be happy to know.

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ROCR documentation built on May 2, 2020, 5:05 p.m.