evaluate_test_metrics: Evaluate Test Metrics for a Grid Search Model

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

Evaluate Test Metrics for a Grid Search Model

Description

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

Usage

evaluate_test_metrics(grid_search, X_test, y_test, modules)

Arguments

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

Value

A list containing key performance metrics of the best model: - @field precision: The weighted precision score. - @field recall: The weighted recall score. - @field f1: The weighted F1 score. - @field accuracy: The overall accuracy score. These metrics are crucial for evaluating the effectiveness of the model on test data.

GeneSelectR documentation built on May 29, 2024, 4:01 a.m.