I understand that you want to calculate the accuracy of the classification for each fold and then compute the mean and standard deviation.
To achieve this, you can follow these steps:
- Use the “crossval” function to perform k-fold cross-validation on your decision tree classifier model (Mdl). Set the number of folds to 5 using the “kFold” option.
- After cross-validation is complete, you can use the “kfoldPredict” function to obtain the predicted labels for each fold.
- Compare the predicted labels with the true labels (y) to calculate the accuracy for each fold.
- Calculate the mean and standard deviation of the accuracy values obtained from the previous step.
CVMdl = crossval(Mdl, 'kFold', 5);
% Perform k-fold cross-validation predictions
kfold_pred = kfoldPredict(CVMdl);
You can refer to these documentation links for further reference:
