Balancing Multiclass Datasets for Classification Tasks R package scutr version 0.2.0 Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package. Software Depends: R (>= 2.10) Imports: smotefamily, parallel, mclust Suggests: testthat (>= 2.0.0) Keenan Ganz <ganzkeenan1@gmail.com> Comprehensive R Archive Network (CRAN) Keenan Ganz [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=scutr/LICENSE) 2023-11-17 application/tgz https://CRAN.R-project.org/package=scutr doi:10.32614/CRAN.package.scutr