Package

r-rocr

Description

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.

Note