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:
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')) |
- hacide.test - Half circle filled data
- hacide.train - Half circle filled data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:b0d750fcd4. Checks:4 OK, 5 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Apr 01 2025 |
| R-4.5-win | OK | Apr 01 2025 |
| R-4.5-mac | OK | Apr 01 2025 |
| R-4.5-linux | OK | Apr 01 2025 |
| R-4.4-win | NOTE | Apr 01 2025 |
| R-4.4-mac | NOTE | Apr 01 2025 |
| R-4.4-linux | NOTE | Apr 01 2025 |
| R-4.3-win | NOTE | Apr 01 2025 |
| R-4.3-mac | NOTE | Apr 01 2025 |
Exports:accuracy.measovun.sampleroc.curveROSEROSE.eval
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| ROSE: Random Over-Sampling Examples | ROSE-package ROSEpack |
| Metrics to evaluate a classifier accuracy in imbalanced learning | accuracy.meas |
| Half circle filled data | hacide.test hacide.train |
| Over-sampling, under-sampling, combination of over- and under-sampling. | ovun.sample |
| ROC curve | roc.curve |
| Generation of synthetic data by Randomly Over Sampling Examples (ROSE) | ROSE |
| Evaluation of learner accuracy by ROSE | ROSE.eval |
