.universal_algorithms

<p>Internal function returning algorithm identifiers that support both
numerical and categorical features.</p>

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.

.universal_algorithms: Universal Algorithms

Description

Usage

Arguments

Value