R: Balancing Multiclass Datasets for Classification Tasks

# Balancing Multiclass Datasets for Classification Tasks

## Documentation for package ‘scutr’ version 0.2.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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.
oversample_smote | Oversample a dataset by SMOTE.
resample_random | Randomly resample a dataset.
sample_classes | Stratified index sample of different values in a vector.
SCUT | SMOTE and cluster-based undersampling technique.
SCUT_parallel | SMOTE and cluster-based undersampling technique.
undersample_hclust | Undersample a dataset by hierarchical clustering.
undersample_kmeans | Undersample a dataset by kmeans clustering.
undersample_mclust | Undersample a dataset by expectation-maximization clustering
undersample_mindist | Undersample a dataset by iteratively removing the observation with the lowest total distance to its neighbors of the same class.
undersample_tomek | Undersample a dataset by removing Tomek links.
validate_dataset | Validate a dataset for resampling.
wine | Type and chemical analysis of three different kinds of wine.