required_pkgs.step_obwoe

Lists the packages required to execute the step_obwoe transformation.

High-performance implementation of 36 optimal binning algorithms
(16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation,
credit scoring, and risk modeling. Includes advanced methods such as Mixed
Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing,
and Monotonic Regression. Features automatic method selection based on
Information Value ('IV') maximization, strict monotonicity enforcement, and
efficient handling of large datasets via 'Rcpp'. Fully integrated with the
'tidymodels' ecosystem for building robust machine learning pipelines.
Based on methods described in Siddiqi (2006) <doi:10.1002/9781119201731>
and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>.

Lopes J. E.

OptimalBinningWoE

Optimal Binning and Weight of Evidence Framework for Modeling

required_pkgs.step_obwoe function

<dl><dt>x</dt>
<dd>A step_obwoe object.</dd>
<dt>...</dt>
<dd>Additional arguments (currently unused).</dd></dl>

Arguments

Required Packages for step_obwoe — required_pkgs.step_obwoe

<dl>

<dt>x</dt>
<dd>A step_obwoe object.</dd>


<dt>...</dt>
<dd>Additional arguments (currently unused).</dd>

</dl>

required_pkgs.step_obwoe: Required Packages for step_obwoe

Description

Usage

Value

Arguments