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

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

Peer review:

Datasets:

On CRAN:

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

5 exports 4 stars 6.21 score 0 dependencies 2 dependents 131 mentions 1.4k scripts 11.2k downloads

Last updated 3 years agofrom:b0d750fcd4. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 30 2024
R-4.5-winNOTEAug 30 2024
R-4.5-linuxNOTEAug 30 2024
R-4.4-winNOTEAug 30 2024
R-4.4-macNOTEAug 30 2024
R-4.3-winNOTEAug 30 2024
R-4.3-macNOTEAug 30 2024

Exports:accuracy.measovun.sampleroc.curveROSEROSE.eval

Dependencies: