biomor_benchmark

Evaluates a trained caret model on test data, returning
Accuracy, F1 score, and ROC-AUC. If only one class is present
in the test set, ROC-AUC is returned as NA.

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

biomor_benchmark function

<dl><dt>model</dt>
<dd>A trained caret model</dd>
<dt>test_data</dt>
<dd>Dataframe containing predictors and outcome</dd>
<dt>outcome_col</dt>
<dd>Name of outcome column</dd></dl>

Arguments

Benchmark a trained model — biomor_benchmark

<dl>

<dt>model</dt>
<dd>A trained caret model</dd>


<dt>test_data</dt>
<dd>Dataframe containing predictors and outcome</dd>


<dt>outcome_col</dt>
<dd>Name of outcome column</dd>

</dl>

biomor_benchmark: Benchmark a trained model

Description

Usage

Value

Arguments