resample_random

This function is used to resample a dataset by randomly removing or duplicating rows. It is usable for both oversampling and undersampling.

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

resample_random function

<dl><dt>data</dt>
<dd>Dataframe to be resampled.</dd>
<dt>cls</dt>
<dd>Class that should be randomly resampled.</dd>
<dt>cls_col</dt>
<dd>Column containing class information.</dd>
<dt>m</dt>
<dd>Desired number of samples.</dd></dl>

Arguments

Randomly resample a dataset. — resample_random

<dl>

<dt>data</dt>
<dd>Dataframe to be resampled.</dd>


<dt>cls</dt>
<dd>Class that should be randomly resampled.</dd>


<dt>cls_col</dt>
<dd>Column containing class information.</dd>


<dt>m</dt>
<dd>Desired number of samples.</dd>

</dl>

resample_random: Randomly resample a dataset.

Description

Usage

Value

Arguments

Examples