plot.obwoe_gains

Visualizes gains table metrics including cumulative capture curves,
KS plot, and lift chart.

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

plot.obwoe_gains function

<dl><dt>x</dt>
<dd>An object of class <code>"obwoe_gains"</code>.</dd>
<dt>type</dt>
<dd>Character string: <code>"cumulative"</code> (default), <code>"ks"</code>,
<code>"lift"</code>, or <code>"woe_iv"</code>.</dd>
<dt>...</dt>
<dd>Additional arguments passed to plotting functions.</dd></dl>

Arguments

Plot Gains Table — plot.obwoe_gains

<dl>

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


<dt>type</dt>
<dd>Character string: <code>"cumulative"</code> (default), <code>"ks"</code>,
<code>"lift"</code>, or <code>"woe_iv"</code>.</dd>


<dt>...</dt>
<dd>Additional arguments passed to plotting functions.</dd>

</dl>

plot.obwoe_gains: Plot Gains Table

Description

Usage

Value

Arguments