ROCR: Visualizing the Performance of Scoring Classifiers
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-parameterized 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 parameterization 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
parameters.
Downloads:
Reverse dependencies:
| Reverse depends: |
Comp2ROC, cvAUC, genMOSS, iFad, MEET, obliqueRF, pathClass, PredictABEL, RcmdrPlugin.ROC, rocc, sperrorest, subtype |
| Reverse imports: |
aLFQ, AutoModel, Biocomb, btergm, DMwR, EMP, gkmSVM, hybridEnsemble, lulcc, mlDNA, MRIaggr, MXM, netClass, OmicKriging, RFmarkerDetector |
| Reverse suggests: |
AER, dismo, mlr, rattle, recommenderlab, sand, SuperLearner, TDMR, unbalanced |