Hi,
Currently, you have to split the data into training and validation manually to generate results on validation set.
To use 10-fold cross-validation, you can fit the model on 90% of the data, and compute results for the remaining 10% of data which was not used for fitting. You can then loop over each of the 10 subsets to plot the ROC curves for individual runs.
Here is a small example on how to do that:
k = 10;
cvFolds = crossvalind('Kfold', labels, k);
for i = 1:k
testIdx = (cvFolds == i); % get indices of test instances
trainIdx = ~testIdx; % get indices training instances
classificationSVM = fitcsvm(data_features(trainIdx,:), labels(trainIdx), ...
'KernelFunction', 'linear', ...
'PolynomialOrder', [], ...
'BoxConstraint', 1, ...
'Standardize', true);
[predictions, score] = predict(svmModel, data_features(testIdx,:));
[X,Y] = perfcurve(labels(trainIdx), predictions,'versicolor');
plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC curve')
end
Hope this helps!
