How to do n-fold cross-validation in KNN

[kisumsam]

Is there any option in Proc Discrim (or another KNN procedure) that can do n-fold cross validation?

Just some quick background. I'm trying to use KNN to classify the fishes in the SASHelp.Fish data set. Below is the code:

data analysis;
set sashelp.fish;
where species in ('Bream', 'Perch');
run;

data train test;
set analysis;
rand = ranuni(100);
if rand <= 0.8 then output train;
else output test;
run;

Above code splits the FISH data set into training and testing data set. In the training set, I want to do n-fold cross-validation to get the optimal k for KNN. See link below:

https://medium.com/@svanillasun/how-to-deal-with-cross-validation-based-on-knn-algorithm-compute-auc...

I can't seem to find the Proc Discrim options that enable me to do this easily.

proc discrim data = train test = test
  testout = _score1 method = npar k = 5 testlist crossvalidate crosslist;
  class species;
  var weight height;
run; 

Does anyone know whether this cross-validation feature is available in Proc Discrim (or any other procedure)? If not, what's the better way to find the optimal k for KNN?

[PaigeMiller]

The documentation lists three different crossvalidation options.

https://documentation.sas.com/?docsetId=statug&docsetVersion=15.1&docsetTarget=statug_discrim_syntax...