compute_f1_threshold

Sweeps thresholds between 0 and 1 to find the one that maximizes F1.

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

compute_f1_threshold function

<dl><dt>y_true</dt>
<dd>True factor labels.</dd>
<dt>y_prob</dt>
<dd>Predicted probabilities for the positive class.</dd>
<dt>positive</dt>
<dd>Name of the positive class (default <code>"Active"</code>).</dd></dl>

Arguments

Compute optimal threshold for maximum F1 score — compute_f1_threshold

<dl>

<dt>y_true</dt>
<dd>True factor labels.</dd>


<dt>y_prob</dt>
<dd>Predicted probabilities for the positive class.</dd>


<dt>positive</dt>
<dd>Name of the positive class (default <code>"Active"</code>).</dd>

</dl>

compute_f1_threshold: Compute optimal threshold for maximum F1 score

Description

Usage

Value

Arguments