performance_score_r2

Internal function to compute the R-squared of observations versus 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_r2 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

Pearson's R-squared of Observations vs Predictions — performance_score_r2

<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_r2: Pearson's R-squared of Observations vs Predictions

Description

Usage

Value

Arguments

See Also