How to use cross validation/ leave one out in algorithm

How can I use cross validation/Leave one out in following example https://in.mathworks.com/help/deeplearning/ug/train-stacked-autoencoders-for-image-classification.html

Answer 1

[Pranav Verma]

Hi Chhavi,

The cvpartition(group,'KFold',k) function with k=n creates a random partition for leave-one-out cross-validation on n observations. Below example demonstrates the aforementioned function,

load('fisheriris');
CVO = cvpartition(species,'k',150); %number of observations 'n' = 150
err = zeros(CVO.NumTestSets,1);
for i = 1:CVO.NumTestSets
    trIdx = CVO.training(i);
    teIdx = CVO.test(i);
    ytest = classify(meas(teIdx,:),meas(trIdx,:),...
		 species(trIdx,:));
    err(i) = sum(~strcmp(ytest,species(teIdx)));
end
cvErr = sum(err)/sum(CVO.TestSize);

Alternatively, you can use cvpartition(n,'LeaveOut') leave-one-out cross-validation.

For further information about the cross-validation in MATLAB, please refer to the link: https://www.mathworks.com/help/stats/cvpartition.html

[Chhavi Bharti]

@Pranav Verma Hi pranav I tried this code. But this is randomly doing the partition. How cound i get an index for tested data?

fold=cvpartition(label,'LeaveOut');

cp=classperf(label);

confmat=0;

for k=1:size(label,2)

trainIdx=fold.training(k); testIdx=fold.test(k);

xtrainc=imgs(trainIdx); ytrainc=label(trainIdx);

xtestc=imgs(testIdx); ytestc=label(testIdx);

xTrainImages=xtrainc';

tTrain=ytrainc;

xTestImages=xtestc';

tTest=ytestc;

%%DNN model

[c,cm,ind,per]=confusion(tTest,y); % y is output of DNN model

end