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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.

Versions across snapshots

VersionRepositoryFileSize
0.2.0 rolling linux/jammy R-4.5 scutr_0.2.0.tar.gz 237.6 KiB
0.2.0 rolling linux/noble R-4.5 scutr_0.2.0.tar.gz 237.6 KiB
0.2.0 rolling source/ R- scutr_0.2.0.tar.gz 203.6 KiB
0.2.0 latest linux/jammy R-4.5 scutr_0.2.0.tar.gz 237.6 KiB
0.2.0 latest linux/noble R-4.5 scutr_0.2.0.tar.gz 237.6 KiB
0.2.0 latest source/ R- scutr_0.2.0.tar.gz 203.6 KiB
0.2.0 2026-04-26 source/ R- scutr_0.2.0.tar.gz 203.6 KiB
0.2.0 2026-04-23 source/ R- scutr_0.2.0.tar.gz 203.6 KiB
0.2.0 2026-04-09 windows/windows R-4.5 scutr_0.2.0.zip 241.5 KiB
0.2.0 2025-04-20 source/ R- scutr_0.2.0.tar.gz 203.6 KiB

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