tidy.step_obwoe

Returns a tibble with information about the binning transformation. For
trained steps, returns one row per bin per feature, including bin labels,
WoE values, and IV contributions. For untrained steps, returns a placeholder
tibble.

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

tidy.step_obwoe function

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

Arguments

Tidy Method for step_obwoe — tidy.step_obwoe

<dl>

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


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

</dl>

tidy.step_obwoe: Tidy Method for step_obwoe

Description

Usage

Value

Arguments