prep.step_obwoe

Fits the optimal binning models on training data. This method is called
by <code><a href="https://recipes.tidymodels.org/reference/prep.html">prep</a></code> and should not be invoked directly.

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

prep.step_obwoe function

<dl><dt>x</dt>
<dd>A step_obwoe object.</dd>
<dt>training</dt>
<dd>A tibble or data frame containing the training data.</dd>
<dt>info</dt>
<dd>A tibble with column metadata from the recipe.</dd>
<dt>...</dt>
<dd>Additional arguments (currently unused).</dd></dl>

Arguments

Fits the optimal binning models on training data. This method is called
by <code><a href='https://recipes.tidymodels.org/reference/prep.html'>prep</a></code> and should not be invoked directly.

Prepare the Optimal Binning Step — prep.step_obwoe

<dl>

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


<dt>training</dt>
<dd>A tibble or data frame containing the training data.</dd>


<dt>info</dt>
<dd>A tibble with column metadata from the recipe.</dd>


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

</dl>

prep.step_obwoe: Prepare the Optimal Binning Step

Description

Usage

Value

Arguments