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scutr  

Balancing Multiclass Datasets for Classification Tasks
View on CRAN: Click here


Download and install scutr package within the R console
Install from CRAN:
install.packages("scutr")

Install from Github:
library("remotes")
install_github("cran/scutr")

Install by package version:
library("remotes")
install_version("scutr", "0.2.0")



Attach the package and use:
library("scutr")
Maintained by
Keenan Ganz
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-06-24
Latest Update: 2023-11-17
Description:
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) . Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.
How to cite:
Keenan Ganz (2021). scutr: Balancing Multiclass Datasets for Classification Tasks. R package version 0.2.0, https://cran.r-project.org/web/packages/scutr. Accessed 05 Jun. 2026.
Previous versions and publish date:
0.1.2 (2021-06-24 13:40)
Other packages that cited scutr R package
View scutr citation profile
Other R packages that scutr depends, imports, suggests or enhances
Complete documentation for scutr
Functions, R codes and Examples using the scutr R package
Some associated functions: SCUT . bullseye . imbalance . oversample_smote . resample_random . sample_classes . undersample_hclust . undersample_kmeans . undersample_mclust . undersample_mindist . undersample_tomek . validate_dataset . wine . 
Some associated R codes: data.R . oversample.R . scut.R . undersample.R .  Full scutr package functions and examples
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