[R-pkgs] New package: ROCR (Visualizing classifier performance)
tobias.sing at mpi-sb.mpg.de
Mon Feb 28 15:03:50 CET 2005
Dear R users,
we are glad to announce the release of our new R package ROCR, for visualizing
the performance of scoring classifiers (available on CRAN). We hope that the
package might be useful for those of you working on classification problems.
For details, see the package description below, or the ROCR website:
http://rocr.bioinf.mpi-sb.mpg.de. You can get a short overview by typing
'demo(ROCR)'. Any kind of feedback (questions, comments, suggestions, bug
reports) is very welcome.
the ROCRs (Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer)
ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall
plots are popular examples of trade-off visualizations for specific pairs of
performance measures. ROCR is a flexible tool for creating
cutoff-parametrized 2D performance curves by freely combining two from over
25 performance measures (new performance measures can be added using a
standard interface). Curves from different cross-validation or bootstrapping
runs can be averaged by different methods, and standard deviations, standard
errors or box plots can be used to visualize the variability across the runs.
The parametrization can be visualized by printing cutoff values at the
corresponding curve positions, or by coloring the curve according to cutoff.
All components of a performance plot can be quickly adjusted using a flexible
parameter dispatching mechanism. Despite its flexibility, ROCR is easy to
use, with only three commands and reasonable default values for all optional
Tobias Sing phone: +49 681 9325 315
Max-Planck-Institut für Informatik fax : +49 681 9325 399
Stuhlsatzenhausweg 85 email: tobias.sing at mpi-sb.mpg.de
66123 Saarbrücken, Germany web : http://www.tobiassing.net
More information about the R-packages