Hello Roozbeh,
Please try the following code
% let X,Y be train features and target
%xtest, ytest be test features and targets
k = 5;
% k for number of nearest neighbours parameter
metric = 'euclidean';
weights = 'uniform';
mdl = kNNeighborsRegressor(k,metric,weights);
indices = crossvalind('Kfold',Y,5);
for i = 1:5
test = (indices == i);
train = ~test;
mdl = mdl.fit(X(train,:),Y(train));
Ypred = mdl.predict(X(test,:);
error = loss(mdl,Ypred,Y(test))
% you can either display is error or save it in a array
end
%The following code can be used to calculate error on test data
ypred = mdl.predict(xtest)
test_error = loss(mdl,ypred,ytest)
Please refer to this links for more information on Knn for regression and k-fold cross validation:
I hope this information helps and please reach out for any further issues.
