vi_predictors

<p>All Predictor Names in Example Data Frame vi</p>

datasets

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

vi_predictors function

<p>Character vector with predictor names.</p>

vi_predictors: All Predictor Names in Example Data Frame vi

Description

Usage

Arguments

Format

See Also