K-Nearest Neighbors (KNN) Classification

This function applies KNN classification to the harmonized data in the input precision object containing. It supports two thresholding methods: cross-validation to optimize the number of neighbors (k) or a fixed k value of 1.

Usage

classification.knn(object, threshold_method = "cv", kfold = 5, folds = NULL)

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 a fixed k = 1.

kfold

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

folds

An optional list of pre-specified folds for cross-validation. If provided, these folds will be used instead of generating new ones.

Value

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