performance_score_auc

Internal function to compute the AUC of binomial models within <code>preference_order()</code>. As it is build for speed, this function does not check the inputs.

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_auc function

<dl><dt>o</dt>
<dd>(required, binomial vector) Binomial response with values 0 and 1. Default: NULL</dd>
<dt>p</dt>
<dd>(required, numeric vector) Predictions of binomial model. Default: NULL</dd></dl>

Arguments

Area Under the Curve of Binomial Observations vs Probabilistic Model Predictions — performance_score_auc

<dl>

<dt>o</dt>
<dd>(required, binomial vector) Binomial response with values 0 and 1. Default: NULL</dd>


<dt>p</dt>
<dd>(required, numeric vector) Predictions of binomial model. Default: NULL</dd>

</dl>

performance_score_auc: Area Under the Curve of Binomial Observations vs Probabilistic Model Predictions

Description

Usage

Value

Arguments

See Also