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-4.5)ROSE_0.0-4.zip(r-4.4)ROSE_0.0-4.zip(r-4.3)
ROSE_0.0-4.tgz(r-4.5-any)ROSE_0.0-4.tgz(r-4.4-any)ROSE_0.0-4.tgz(r-4.3-any)
ROSE_0.0-4.tar.gz(r-4.5-noble)ROSE_0.0-4.tar.gz(r-4.4-noble)
ROSE_0.0-4.tgz(r-4.4-emscripten)ROSE_0.0-4.tgz(r-4.3-emscripten)
ROSE.pdf |ROSE.html
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:

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

6.54 score 4 stars 2 packages 1.6k scripts 8.8k downloads 131 mentions 5 exports 0 dependencies

Last updated 4 years agofrom:b0d750fcd4. Checks:4 OK, 4 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 31 2025
R-4.5-winOKJan 31 2025
R-4.5-macOKJan 31 2025
R-4.5-linuxOKJan 31 2025
R-4.4-winNOTEJan 31 2025
R-4.4-macNOTEJan 31 2025
R-4.3-winNOTEJan 31 2025
R-4.3-macNOTEJan 31 2025

Exports:accuracy.measovun.sampleroc.curveROSEROSE.eval

Dependencies: