performance_score_v

Internal function to compute the Cramer's V of categorical observations versus categorical model predictions.

Effortless multicollinearity management in data frames with both numeric and categorical variables for statistical and machine learning applications. The package simplifies multicollinearity analysis by combining four robust methods: 1) target encoding for categorical variables (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); 2) automated feature prioritization to prevent key variable loss during filtering; 3) pairwise correlation for all variable combinations (numeric-numeric, numeric-categorical, categorical-categorical); and 4) fast computation of variance inflation factors.

Blas M. Benito

collinear

Automated Multicollinearity Management

performance_score_v function

<dl><dt>o</dt>
<dd>(required, numeric vector) Response values. Default: NULL</dd>
<dt>p</dt>
<dd>(required, numeric vector) Model predictions. Default: NULL</dd></dl>

Arguments

Cramer's V of Observations vs Predictions — performance_score_v

<dl>

<dt>o</dt>
<dd>(required, numeric vector) Response values. Default: NULL</dd>


<dt>p</dt>
<dd>(required, numeric vector) Model predictions. Default: NULL</dd>

</dl>

performance_score_v: Cramer's V of Observations vs Predictions

Description

Usage

Value

Arguments

See Also