GeneralCor

Feature selection algorithm that extracts features in highly
correlated spaces. The extracted features are meant to be fed into
simple explainable models such as linear or logistic regressions. The
package is useful in the field of explainable modelling as a
way to understand variable behavior.

Allen Sunny

TangledFeatures

Feature Selection in Highly Correlated Spaces

GeneralCor function

<dl><dt>df</dt>
<dd>The imported Data Frame</dd>
<dt>cor1</dt>
<dd>The correlation metric between two continuous features. Defaults to pearson</dd>
<dt>cor2</dt>
<dd>The correlation metric between one categorical feature and one cont feature. Defaults to biserial</dd>
<dt>cor3</dt>
<dd>The correlation metric between two categorical features. Defaults to Cramers-V</dd></dl>

Arguments

Generalized Correlation function — GeneralCor

<dl>

<dt>df</dt>
<dd>The imported Data Frame</dd>


<dt>cor1</dt>
<dd>The correlation metric between two continuous features. Defaults to pearson</dd>


<dt>cor2</dt>
<dd>The correlation metric between one categorical feature and one cont feature. Defaults to biserial</dd>


<dt>cor3</dt>
<dd>The correlation metric between two categorical features. Defaults to Cramers-V</dd>

</dl>

GeneralCor: Generalized Correlation function

Description

Usage

Value

Arguments

Examples