imbalance

<p>An imbalanced dataset with randomly placed normal distributions around the origin.
The nth class has n * 10 observations.</p>

datasets

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.

Keenan Ganz

scutr

Balancing Multiclass Datasets for Classification Tasks

imbalance function

<p>a data.frame with 2100 rows and 11 columns</p>

imbalance: An imbalanced dataset with randomly placed normal distributions around the origin. The nth class has n * 10 observations.

Description

Usage

Arguments

Format