Identify the observations that are in the test (holdout) set of a cvpartition object.

Partition 10 observations for holdout validation. Select approximately 30% of the observations to be in the test set.

rng('default') % For reproducibility
c = cvpartition(10,'Holdout',0.30)

c = 
Hold-out cross validation partition
   NumObservations: 10
       NumTestSets: 1
         TrainSize: 7
          TestSize: 3

Identify the test set observations. Observations that correspond to 1s are in the test set.

holdout = test(c)

holdout = 10x1 logical array

   0
   0
   0
   1
   0
   0
   0
   0
   1
   1

Visualize the results. The fourth, ninth, and tenth observations are in the test set.

h = heatmap(double(holdout),'ColorbarVisible','off');
sorty(h,'1','descend')
ylabel('Observation')
title('Test Set Observations')

Identify the observations that are in the test sets, or folds, of a cvpartition object for 3-fold cross-validation.

Partition 10 observations for 3-fold cross-validation. Notice that c contains three repetitions of training and test data.

rng('default') % For reproducibility
c = cvpartition(10,'KFold',3)

c = 
K-fold cross validation partition
   NumObservations: 10
       NumTestSets: 3
         TrainSize: 7  6  7
          TestSize: 3  4  3

Identify the test set observations for each repetition of training and test data. Observations that correspond to 1s are in the corresponding test set (fold).

fold1 = test(c,1)

fold1 = 10x1 logical array

   1
   1
   0
   0
   0
   0
   0
   0
   1
   0

fold2 = test(c,2);
fold3 = test(c,3);

Visualize the results. The first, second, and ninth observations are in the first test set. The third, sixth, eighth, and tenth observations are in the second test set. The fourth, fifth, and seventh observations are in the third test set.

data = [fold1,fold2,fold3];
h = heatmap(double(data),'ColorbarVisible','off');
sorty(h,{'1','2','3'},'descend')
xlabel('Repetition')
ylabel('Observation')
title('Test Set Observations')