bake.step_obwoe

Applies the learned binning and WoE transformation to new data. This method
is called by <code><a href="https://recipes.tidymodels.org/reference/bake.html">bake</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

bake.step_obwoe function

<dl><dt>object</dt>
<dd>A trained step_obwoe object.</dd>
<dt>new_data</dt>
<dd>A tibble or data frame to transform.</dd>
<dt>...</dt>
<dd>Additional arguments (currently unused).</dd></dl>

Arguments

Applies the learned binning and WoE transformation to new data. This method
is called by <code><a href='https://recipes.tidymodels.org/reference/bake.html'>bake</a></code> and should not be invoked directly.

Apply the Optimal Binning Transformation — bake.step_obwoe

<dl>

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


<dt>new_data</dt>
<dd>A tibble or data frame to transform.</dd>


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

</dl>

bake.step_obwoe: Apply the Optimal Binning Transformation

Description

Usage

Value

Arguments