validate_dataset

This functions checks that the given column is present in the data and that all columns besides the class column are numeric.

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

validate_dataset function

<dl><dt>data</dt>
<dd>Dataframe to validate.</dd>
<dt>cls_col</dt>
<dd>Column with class information.</dd></dl>

Arguments

Validate a dataset for resampling. — validate_dataset

<dl>

<dt>data</dt>
<dd>Dataframe to validate.</dd>


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

</dl>

validate_dataset: Validate a dataset for resampling.

Description

Usage

Value

Arguments