KNN: Classification using k-NN

In fdm2id: Data Mining and R Programming for Beginners

Classification using k-NN

Description

This function builds a classification model using Logistic Regression.

Usage

KNN(
  train,
  labels,
  k = 1:10,
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

train	The training set (description), as a data.frame.
labels	Class labels of the training set (vector or factor).
k	The k parameter.
nfolds	The number of folds of the cross-validation a method runs to choose its hyperparameters. Only used when there is something to choose, i.e. when one of them is given as a vector. Lower it to fit faster, at the cost of a noisier choice.
tune	If true, the function returns parameters instead of a classification model.
methodparameters	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: KNN does not support reusing pre-tuned parameters (it stores the training set and re-tunes k on every call when k is a vector).
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: KNN does not produce a plot.
seed	A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.
...	Other parameters.

Value

The classification model.

See Also

knn

Examples

require (datasets)
data (iris)
KNN (iris [, -5], iris [, 5])

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.