RomeroBarata/bimba: Sampling Algorithms for Two-Class Imbalanced Data Sets
Sampling algorithms to aid classifiers in learning from two-class imbalanced data sets. Over-sampling, under-sampling, and hybrid algorithms are included. In addition, sampling algorithms that are composed of a sequence of already existing algorithms can be easily created.
Getting started
Browse package contents
Package details |
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| Maintainer | |
| License | MIT + file LICENSE |
| Version | 0.0.0.9000 |
| URL | https://github.com/RomeroBarata/bimba |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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