Using Classification and subsets

I'm trying to use the Discriminant Analysis to classify 4 groups from a pool of data, and I've encountered an issue I'm unable to solve.

I've used the following code sample from the 'Classification' page:

lda = fitcdiscr(My_Data,species);
ldaClass = resubPredict(lda);
ldaResubErr = resubLoss(lda); %resubstitution error
cp = cvpartition(species,'KFold',10);
cvlda = crossval(lda,'CVPartition',cp);
ldaCVErr = kfoldLoss(cvlda);

Now, my question is this: is it possible to know what part of the data is being used as the training set, and what part is being used for the classification? To clarify, after I use 'cvpartition' for say, 10 subsets, I can 10x2 groups where for each subset I have a 'training set' and a 'classification set', but I can't seem to find any way to determine which is which.

I hope my question is clear enough. Thank you for your help and time!

Answer 1

[Sean de Wolski]

Look at the training and test methods of the cvpartition.

cv = cvpartition(100,'KFold',3);
train2 = training(cv,2) % 2nd fold training set is true

http://www.mathworks.com/help/releases/R2015b/stats/cvpartition-class.html