Package: ROSE 0.0-4

ROSE: Random Over-Sampling Examples

Functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.

Authors:Nicola Lunardon, Giovanna Menardi, Nicola Torelli

ROSE_0.0-4.tar.gz
ROSE_0.0-4.zip(r-4.7)ROSE_0.0-4.zip(r-4.6)ROSE_0.0-4.zip(r-4.5)
ROSE_0.0-4.tgz(r-4.6-any)ROSE_0.0-4.tgz(r-4.5-any)
ROSE_0.0-4.tar.gz(r-4.7-any)ROSE_0.0-4.tar.gz(r-4.6-any)
ROSE_0.0-4.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
ROSE/json (API)

# Install 'ROSE' in R:
install.packages('ROSE', repos = c('https://nicolalunardon.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

7.12 score 4 stars 3 packages 1.9k scripts 19k downloads 131 mentions 5 exports 0 dependencies

Last updated from:b0d750fcd4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK136
source / vignettesOK123
linux-release-x86_64OK107
macos-release-arm64OK159
macos-oldrel-arm64OK175
windows-develOK69
windows-releaseOK78
windows-oldrelOK63
wasm-releaseOK85

Exports:accuracy.measovun.sampleroc.curveROSEROSE.eval

Dependencies: