CRAN/E | ROCR

ROCR

Visualizing the Performance of Scoring Classifiers

Installation

About

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.

Citation ROCR citation info
ipa-tys.github.io/ROCR/
Bug report File report

Key Metrics

Version 1.0-11
R ≥ 3.6
Published 2020-05-02 1426 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks ROCR results

Downloads

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Maintainer

Maintainer

Felix G.M. Ernst

felix.gm.ernst@outlook.com

Authors

Tobias Sing

aut

Oliver Sander

aut

Niko Beerenwinkel

aut

Thomas Lengauer

aut

Thomas Unterthiner

ctb

Felix G.M. Ernst

cre

Material

README
NEWS
Reference manual
Package source

In Views

MachineLearning

Vignettes

ROCR

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

ROCR archive

Depends

R ≥ 3.6

Imports

methods
graphics
grDevices
gplots
stats

Suggests

testthat
knitr
rmarkdown

Reverse Depends

ASpediaFI
Comp2ROC
EBPRS
EMP
GsymPoint
maPredictDSC
rocc
SLModels
swa
utiml

Reverse Imports

a4Classif
aLFQ
alookr
Anaquin
BayesPostEst
Biocomb
btergm
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classifierplots
compcodeR
CSMES
cvAUC
DCD
DeSousa2013
DRaWR
DrugClust
DWLS
easier
EFS
envi
erccdashboard
eSDM
EZtune
fdm2id
funModeling
gkmSVM
hybridEnsemble
iCOBRA
influenceAUC
iRafNet
jmv
LedPred
LOGANTree
lulcc
mccf1
Melissa
MetaIntegrator
MLmetrics
netDx
nlcv
nlnet
nlpred
nproc
OmicKriging
PAA
PathoStat
Pi
prcbench
PredictABEL
Load all 65 items
(warning: might lead to performance issues and take some time)

Reverse Suggests

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