yh_kNN: kNN or NN prediction
In gencve: General Cross Validation Engine

Description Usage Arguments Value Author(s) See Also Examples

Given training data and test examples, the kNN predictions for the test data are produced. The tuning parameter k is automatically selected by specifying one of the methods: LOOCV, MLE or NN.

1	yh_kNN(dfTr, dfTe, method = c("LOOCV", "MLE", "NN"), k=1)

`dfTr`	dataframe with last column for the output. The output must be a factor.
`dfTe`	dataframe for test data. Must have columns corresponding to the training columns except the test output is not needed.
`method`	One of the automatic methods for selecting k, the number of nearest neighbours. The default is LOOCV.
`k`	Pre-specified k but this value of k is only used when method="NN" otherwise when method="LOOCV" or method="MLE", k is estimated.

The mis-classification rate (cost) and correlation of prediction and test.

A. I. McLeod

yh_C50, yh_CART, yh_RF, yh_svm, yh_NB, yh_lda, yh_logistic, yh_qda

1 2	yh_kNN(SinghTrain [,c(1:10, 101)], SinghTest [,c(1:10, 101)])#0.088 yh_kNN(SinghTrain [,c(1:10, 101)], SinghTest [,c(1:10, 101)], method="NN")#0.088