Hi,
I was trying to get the optimal 'k' for the knn. To do this I was using the
following function :
knn.cvk <- function(datmat, cl, k = 2:9) {
datmatT <- (datmat)
cv.err <- cl.pred <- c()
for (i in k) {
newpre <- as.vector(knn.cv(datmatT, cl, k = i))
cl.pred <- cbind(cl.pred, newpre)
cv.err <- c(cv.err, sum(cl != newpre))
}
k0 <- k[which.min(cv.err)]
print(k0)
return(k0)
}
However, the knn.cv function does a 'leave one out' cross validation. I
checked the documentation to see if I could change this, but it appears that I
cannot. Since I have large datasets, I would like to do 10 fold cross
validation, instead of the 'leave one out'.
Is there some other function that I can use that will give me a 10 fold cross
validation for KNN ?
many thanks.
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