npsm (version 2.0.1)

knn_cv: 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))

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

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).

Author

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