How does crossval (for k-fold CV) work in MATLAB after training a classifier?

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To my knowledge, k-fold CV is a technique for model selection where the data is first divided into k-folds where the data in each fold is stratified. Now, consider the following code:
trainedClassifier = fitcnb(X, Y);
partitionedModel = crossval(trainedClassifier, 'KFold', 10);
accuracy = 1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError');
The above code first trains the data in matrix X as per the class labels in vector Y. The trainedClassifier is then used in the function crossval(). My doubt is very simple. Does this line of code
partitionedModel = crossval(trainedClassifier, 'KFold', 10);
divide the matrix X into ten folds and then trains on 9 folds, testes on the remaining fold and this is repeated 10 times with each fold as test matrix or does it simply use the trainedClassifier that was trained in the previous line on the whole matrix X and then testes on each fold as I can only see that the fitcnb has been used only once. Does the function crossval() works upon it internally? If it doesn't, then the training is being done on the whole data instead of on the 9 folds in each iteration as is defined by cross-validation.
Fellow members of the community, I will be highly obliged if this doubt of mine can be cleared. Thanking you in anticipation.
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Answers (3)

Don Mathis
Don Mathis on 30 Nov 2018
The answer is that it divides the dataset into 10 folds and trains the model 10 times on 9 folds each time, using the remaining fold as the test set. The only information taken from 'trainedClassifier' are the hyperparameter values, which are used in each of the 10 trainings. 'fitcnb' is not called 10 times, 'ClassificationNaiveBayes.fit' is.
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fatemeh ghorbani
fatemeh ghorbani on 3 Dec 2017
do you find any answer?

James Ratti
James Ratti on 21 Oct 2018
Any answers??

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