auc_score

Computes the AUC score of binary model predictions.

System for seamless management of multicollinearity in data frames
with numeric and/or categorical variables for statistical analysis and
machine learning modeling. The package combines bivariate correlation
(Pearson, Spearman, and Cramer's V) with variance inflation factor analysis,
target encoding to transform categorical variables into numeric (Micci-Barreca, D. 2001
<DOI:10.1145/507533.507538>), and a flexible
feature prioritization method, to deliver a comprehensive
multicollinearity management tool covering a wide range of use cases.

Blas M. Benito

collinear

Seamless Multicollinearity Management

auc_score function

<dl><dt>observed</dt>
<dd>(required, integer) Numeric vector with observations. Valid values are 1 and 0. Must have the same length as <code>predicted</code>. Default: NULL</dd>
<dt>predicted</dt>
<dd>(required, numeric) Numeric vector in the range 0-1 with binary model predictions. Must have the same length as <code>observed</code>.</dd></dl>

Arguments

Area Under the Receiver Operating Characteristic — auc_score

<dl>

<dt>observed</dt>
<dd>(required, integer) Numeric vector with observations. Valid values are 1 and 0. Must have the same length as <code>predicted</code>. Default: NULL</dd>


<dt>predicted</dt>
<dd>(required, numeric) Numeric vector in the range 0-1 with binary model predictions. Must have the same length as <code>observed</code>.</dd>

</dl>

auc_score: Area Under the Receiver Operating Characteristic

Description

Usage

Value

Arguments

Examples