get_cv_control

Creates a <a href="/link/caret%3A%3AtrainControl?package=BioMoR&version=0.1.1" data-mini-rdoc="BioMoR::caret::trainControl">caret::trainControl</a> object for cross-validation,
configured for two-class problems, ROC-based performance, and optional
sampling strategies such as SMOTE or ROSE.

Tools for bioinformatics modeling using recursive transformer-inspired
architectures, autoencoders, random forests, XGBoost, and stacked ensemble models.
Includes utilities for cross-validation, calibration, benchmarking, and threshold
optimization in predictive modeling workflows. The methodology builds on ensemble
learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and
Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov
2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches
such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).

MD. Arshad

BioMoR

Bioinformatics Modeling with Recursion and Autoencoder-Based
Ensemble

get_cv_control function

<dl><dt>cv</dt>
<dd>Number of folds (default 5).</dd>
<dt>sampling</dt>
<dd>Sampling method (e.g., "smote", "rose", or NULL).</dd></dl>

Arguments

Creates a <a href='https://rdrr.io/pkg/caret/man/trainControl.html'>caret::trainControl</a> object for cross-validation,
configured for two-class problems, ROC-based performance, and optional
sampling strategies such as SMOTE or ROSE.

Get caret cross-validation control — get_cv_control

<dl>

<dt>cv</dt>
<dd>Number of folds (default 5).</dd>


<dt>sampling</dt>
<dd>Sampling method (e.g., "smote", "rose", or NULL).</dd>

</dl>

get_cv_control: Get caret cross-validation control

Description

Usage

Value

Arguments