knn_cv: Train a k nearest neighbors (knn) classifer via cross...

In npsm: Nonparametric Statistical Methods

Train a k nearest neighbors (knn) classifer via cross validation (cv).

Description

Train a k nearest neighbors (knn) classifer via cross validation (cv). The number of folds and the set of the number of neihbors to consider may be specified.

Usage

knn_cv(xy, k.cv = 5, kvec = seq(1, 47, by = 2))

Arguments

xy	Data frame with the data matrix x as the first set of columns and the vector y as the last column.
k.cv	scalar. number of folds to use. default is 5.
kvec	vector. set of neighbors to consider. default is odd integers between 1 and 47 (inclusive).

Value

kvec	set of neighbors considered
error	vector of misclassification error rates corresponding to kvec
k.best	number of neighbors with lowest error rate
k.cv	number of folds to used

Author(s)

John Kloke

References

Hastie, T., Tibshiani, R., and Friedman, J. (2017), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, New York: Springer.

James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013), An Introduction to Statistical Learning with Applications in R, New York: Springer.

Venables, W. N. and Ripley, B. D. (2002) _Modern Applied Statistics with S._ Fourth edition. Springer.

See Also

knn

Examples

train_set <- sim_class2[sim_class2$train==1,-1]
set.seed(19180511)
fit_cv <- knn_cv(train_set,k.cv=10)
fit_cv

npsm documentation built on Nov. 15, 2023, 1:08 a.m.