Source: r-cran-rocr
Maintainer: Debian R Packages Maintainers
Uploaders: Steffen Moeller ,
Andreas Tille ,
Dirk Eddelbuettel
Section: gnu-r
Priority: optional
Build-Depends: debhelper (>= 11~),
dh-r,
r-base-dev,
r-cran-gplots
Standards-Version: 4.1.4
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-rocr
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-rocr.git
Homepage: https://cran.r-project.org/package=ROCR
Package: r-cran-rocr
Architecture: all
Depends: ${R:Depends},
${misc:Depends}
Recommends: ${R:Recommends}
Suggests: ${R:Suggests}
Description: GNU R package to prepare and display ROC curves
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
parameters.
.
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).
.
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.