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How to obtain a ROC curve through cross validation on the out of fold data in cross validation?

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Zeynab Mousavikhamene
Zeynab Mousavikhamene on 8 Jun 2020
I am using fitcsvm and need to obtain ROC curve for the fold that is not used in training.
Here is the code:
classificationSVM = fitcsvm(...
predictors, ...
response, ...
'KernelFunction', 'linear', ...
'PolynomialOrder', [], ...
'BoxConstraint', 1, ...
'Standardize', true, ...
'ClassNames', categorical(classnames_fitcsvm),'KFold',10);
[fitPosteriorSVM,ScoreTransform] = fitPosterior(classificationSVM);
for the prediction step:
I can not use resubPredict because that is set for training data and when I use kfoldPredict I recive error. I need to find the predicted score for the fold that is not used for training in 10 fold cross validation to be able to run perfcurve.
Any idea?


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