evaluate_test_metrics

This function takes a grid search object, test data, and test labels to evaluate the performance
of the best model found during grid search.

The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (<https://scikit-learn.org/stable/index.html>) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) <doi:10.1002/pro.4218>, using 'clusterProfiler' (Wu et al., 2021) <doi:10.1016/j.xinn.2021.100141> and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) <doi:10.1016/j.gpb.2022.04.008>. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.

Damir Zhakparov

GeneSelectR

'GeneSelectR' - Comprehensive Feature Selection Workflow for
Bulk RNAseq Datasets

evaluate_test_metrics function

<dl><dt>grid_search</dt>
<dd>A grid search object containing the best estimator.</dd>
<dt>X_test</dt>
<dd>A data frame or matrix of test features.</dd>
<dt>y_test</dt>
<dd>A vector of test labels.</dd>
<dt>modules</dt>
<dd>A list of Python modules used in the function.</dd></dl>

Arguments

Evaluate Test Metrics for a Grid Search Model — evaluate_test_metrics

<dl>

<dt>grid_search</dt>
<dd>A grid search object containing the best estimator.</dd>


<dt>X_test</dt>
<dd>A data frame or matrix of test features.</dd>


<dt>y_test</dt>
<dd>A vector of test labels.</dd>


<dt>modules</dt>
<dd>A list of Python modules used in the function.</dd>

</dl>

evaluate_test_metrics: Evaluate Test Metrics for a Grid Search Model

Description

Usage

Value

Arguments