sample_classes

Stratified index sample of different values in a vector.

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

sample_classes function

<dl><dt>vec</dt>
<dd>Vector of values to sample from.</dd>
<dt>tot_sample</dt>
<dd>Total number of samples.</dd></dl>

Arguments

Stratified index sample of different values in a vector. — sample_classes

<dl>

<dt>vec</dt>
<dd>Vector of values to sample from.</dd>


<dt>tot_sample</dt>
<dd>Total number of samples.</dd>

</dl>

sample_classes: Stratified index sample of different values in a vector.

Description

Usage

Value

Arguments

Examples