print.step_obwoe

Prints a concise summary of the step_obwoe object.

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

print.step_obwoe function

<dl><dt>x</dt>
<dd>A step_obwoe object.</dd>
<dt>width</dt>
<dd>Maximum width for printing term names.</dd>
<dt>...</dt>
<dd>Additional arguments (currently unused).</dd></dl>

Arguments

Print Method for step_obwoe — print.step_obwoe

<dl>

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


<dt>width</dt>
<dd>Maximum width for printing term names.</dd>


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

</dl>

print.step_obwoe: Print Method for step_obwoe

Description

Usage

Value

Arguments