In order to plot ROC curve, the decision boundary should be a value of the posterior probabilities that you compute using ‘NaiveBayes’. You can plot ROC curves using PERFCURVE, which computes the ROC curve for a vector of classifier predictions, given the true class. More details about PERFCURVE are available via the following link:
Here is a small example of how to plot an ROC curve without cross-validation:
load fisheriris
x = meas(51:end,1:2);
y = species(51:end);
nb = NaiveBayes.fit(x,y);
p = posterior(nb,x);
[X,Y] = perfcurve(y,p(:,1),'versicolor');
plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by naïve Bayes')
To use 10-fold cross-validation, you can fit the model on 90% of the data, and compute predicted posteriors 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.
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