leave one person out cross validation

i have dataset which contains data from 10 subject. My idea fir cross validaytion is leave one person out cross validation. Here i trian on data from 9 subjects and test on data from 1. When we normally do cross validation, we have a stopping criteria which avoids model overfitting.

How do I avoid overfittiing in my case.

below is code snippet

for idx = 1:N%k = LOOCV train on rest; validate on K- meal
    s = [1:idx-1 idx+1:N];
    Xtrain= Training(s); %(all remaining datasets)
    Xvalidate = Training(idx);% idx dataset
    Xtrainlabel = Training_labels(s);
    Xvalidatelabel = Training_labels(idx);
    Mdl = fitcsvm(XTrain(:,featsel),...
        XTrainlabel);
    [trainSVM,trainScoreSVM] = resubPredict(Mdl); %training
    %- Cross-validate the classifier
    CVSVMModel = crossval( Mdl );
    %validation
    Yval_pred= predict(Mdl, XValidate(:, featsel)); %validation
    [cmV,order] = confusionmat(Yval_pred, actual_val);
    tnV = cmV(1,1);
    fnV = cmV(1,2);
    fpV = cmV(2,1);
    tpV = cmV(2,2);
    Accuracy(idx) = (tp+fp)./(tp+fp+tn+fn);
end

[Wan Ji]

Use dropoutLayer may help you avoid model overfitting. Try it

[pallavi patil]

i am using svm as classifier. I supoose dropoutLayer works for neural network.

Answer 1

[Prince Kumar]

You can try the following methods:

1. Remove features
2. Feature Selection
3. Regularization
4. Ensemble models if you are ok with trying models other than SVM