Package: scutr 0.2.0

scutr: Balancing Multiclass Datasets for Classification Tasks

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.

Authors:Keenan Ganz [aut, cre]

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NEWS

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

Peer review:

Bug tracker:https://github.com/s-kganz/scutr/issues

Datasets:
  • bullseye - An imbalanced dataset with a minor class centered around the origin with a majority class surrounding the center.
  • imbalance - An imbalanced dataset with randomly placed normal distributions around the origin. The nth class has n * 10 observations.
  • wine - Type and chemical analysis of three different kinds of wine.

On CRAN:

3.95 score 2 stars 1 packages 15 scripts 184 downloads 10 exports 17 dependencies

Last updated 1 years agofrom:624f415cd4. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winOKNov 04 2024
R-4.5-linuxOKNov 04 2024
R-4.4-winOKNov 04 2024
R-4.4-macOKNov 04 2024
R-4.3-winOKNov 04 2024
R-4.3-macOKNov 04 2024

Exports:oversample_smoteresample_randomsample_classesSCUTSCUT_parallelundersample_hclustundersample_kmeansundersample_mclustundersample_mindistundersample_tomek

Dependencies:clicpp11dbscanFNNgenericsglueigraphlatticelifecyclemagrittrMatrixmclustpkgconfigRcpprlangsmotefamilyvctrs