train_rf

Train a Random Forest model with caret

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

train_rf function

<dl><dt>df</dt>
<dd>A data.frame containing predictors and outcome</dd>
<dt>outcome_col</dt>
<dd>Name of the outcome column (binary factor)</dd>
<dt>ctrl</dt>
<dd>A caret::trainControl object</dd></dl>

Arguments

Train a Random Forest model with caret — train_rf

<dl>

<dt>df</dt>
<dd>A data.frame containing predictors and outcome</dd>


<dt>outcome_col</dt>
<dd>Name of the outcome column (binary factor)</dd>


<dt>ctrl</dt>
<dd>A caret::trainControl object</dd>

</dl>

train_rf: Train a Random Forest model with caret

Description

Usage

Value

Arguments