%0 Journal Article %1 Sing15102005 %A Sing, Tobias %A Sander, Oliver %A Beerenwinkel, Niko %A Lengauer, Thomas %D 2005 %J Bioinformatics %K Classifier ROC Visualization %N 20 %P 3940-3941 %R 10.1093/bioinformatics/bti623 %T ROCR: visualizing classifier performance in R %U http://bioinformatics.oxfordjournals.org/content/21/20/3940.abstract %V 21 %X Summary: ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage.Availability: http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent.Contact: tobias.sing@mpi-sb.mpg.de

@article{Sing15102005, abstract = {Summary: ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage.Availability: http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent.Contact: tobias.sing@mpi-sb.mpg.de}, added-at = {2012-12-14T13:47:27.000+0100}, author = {Sing, Tobias and Sander, Oliver and Beerenwinkel, Niko and Lengauer, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2cca359ee507a4945e2faa779fcd1656e/macek}, doi = {10.1093/bioinformatics/bti623}, eprint = {http://bioinformatics.oxfordjournals.org/content/21/20/3940.full.pdf+html}, interhash = {418a0bc49b0005d766adc1e6a7f68146}, intrahash = {cca359ee507a4945e2faa779fcd1656e}, journal = {Bioinformatics}, keywords = {Classifier ROC Visualization}, number = 20, pages = {3940-3941}, timestamp = {2012-12-14T13:47:27.000+0100}, title = {ROCR: visualizing classifier performance in R}, url = {http://bioinformatics.oxfordjournals.org/content/21/20/3940.abstract}, volume = 21, year = 2005 }