getKNNpredictionFromFormula: Predict classification using KNN

In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

Predict classification using KNN

Description

This function will return the classification of the samples of a test set using a k-nearest neighbors (KNN) algorithm with euclidean distances, given a formula and a train set.

Usage

	getKNNpredictionFromFormula(model.formula,
	                            trainData,
	                            testData,
	                            Outcome = "CLASS",
	                            nk = 3)

Arguments

model.formula	An object of class formula with the formula to be used
trainData	A data frame with the data to train the model, where all variables are stored in different columns
testData	A data frame similar to trainData, but with the data set to be predicted
Outcome	The name of the column in trainData that stores the variable to be predicted by the model
nk	The number of neighbors used to generate the KNN classification

Value

prediction	A vector with the predicted outcome for the testData data set
prob	The proportion of k neighbors that predicted the class to be the one being reported in prediction
binProb	The proportion of k neighbors that predicted the class of the outcome to be equal to 1
featureList	A vector with the names of the features used by the KNN procedure

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya

See Also

predict.fitFRESA, knn

FRESA.CAD documentation built on Nov. 25, 2023, 1:07 a.m.