tunable.step_obwoe

Returns information about which parameters of step_obwoe can be tuned
using the <code>tune</code> package.

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

tunable.step_obwoe function

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

Arguments

Tunable Parameters for step_obwoe — tunable.step_obwoe

<dl>

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


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

</dl>

tunable.step_obwoe: Tunable Parameters for step_obwoe

Description

Usage

Value

Arguments