obwoe_bin_cutoff

A quantitative tuning parameter for the minimum bin support (proportion
of observations per bin) in <code>step_obwoe</code>.

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

obwoe_bin_cutoff function

<dl><dt>range</dt>
<dd>A two-element numeric vector specifying the minimum and maximum
values for the parameter. Default is <code>c(0.01, 0.10)</code>.</dd>
<dt>trans</dt>
<dd>A transformation object from the <code>scales</code> package, or
<code>NULL</code> for no transformation. Default is <code>NULL</code>.</dd></dl>

Arguments

Bin Cutoff Parameter — obwoe_bin_cutoff

<dl>

<dt>range</dt>
<dd>A two-element numeric vector specifying the minimum and maximum
values for the parameter. Default is <code>c(0.01, 0.10)</code>.</dd>


<dt>trans</dt>
<dd>A transformation object from the <code>scales</code> package, or
<code>NULL</code> for no transformation. Default is <code>NULL</code>.</dd>

</dl>

obwoe_bin_cutoff: Bin Cutoff Parameter

Description

Usage

Value

Arguments

Details

See Also

Examples