print.obwoe

Displays a concise summary of optimal binning results, including
the number of successfully processed features and top predictors
ranked by Information Value.

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.obwoe function

<dl><dt>x</dt>
<dd>An object of class <code>"obwoe"</code>.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Print Method for obwoe Objects — print.obwoe

<dl>

<dt>x</dt>
<dd>An object of class <code>"obwoe"</code>.</dd>


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

</dl>

print.obwoe: Print Method for obwoe Objects

Description

Usage

Value

Arguments

See Also