Random Forest Classification

This function applies Random Forest classification to the harmonized data in the input precision object containing. It supports two thresholding methods: cross-validation to optimize the tuning parameter or using default parameters without tuning.

Usage

classification.ranfor(object, threshold_method = "cv", kfold = 5)

Arguments

object

A precision object containing harmonized data. Must contain the slots harmon.train.data with harmonized training data and harmon.test1.data and harmon.test2.data with harmonized test data.

threshold_method

A character string specifying the thresholding method. Use "cv" for cross-validation to determine the optimal k, or "none" to use default parameters without tuning.

kfold

An integer specifying the number of folds for cross-validation. This parameter is only used if threshold_method is set to "cv".

Value

The input object updated with Random Forest classification results added to the classification.result slot, including predicted classes and associated metrics.