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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0-4 |
rolling linux/jammy R-4.5 | ROSE_0.0-4.tar.gz |
110.5 KiB |
0.0-4 |
rolling linux/noble R-4.5 | ROSE_0.0-4.tar.gz |
110.4 KiB |
0.0-4 |
rolling source/ R- | ROSE_0.0-4.tar.gz |
38.9 KiB |
0.0-4 |
latest linux/jammy R-4.5 | ROSE_0.0-4.tar.gz |
110.5 KiB |
0.0-4 |
latest linux/noble R-4.5 | ROSE_0.0-4.tar.gz |
110.4 KiB |
0.0-4 |
latest source/ R- | ROSE_0.0-4.tar.gz |
38.9 KiB |
0.0-4 |
2026-04-26 source/ R- | ROSE_0.0-4.tar.gz |
38.9 KiB |
0.0-4 |
2026-04-23 source/ R- | ROSE_0.0-4.tar.gz |
38.9 KiB |
0.0-4 |
2026-04-09 windows/windows R-4.5 | ROSE_0.0-4.zip |
113.1 KiB |
0.0-4 |
2025-04-20 source/ R- | ROSE_0.0-4.tar.gz |
38.9 KiB |