Why ROC's plotting function perfcurve of MATLAB is yielding 3 ROC curves in case of cross validation?

Question

I plotted 5 fold cross-validation data as a cell array to perfcurve function with positive class=1. Then it generated 3 curves as you can see in the diagram. I was expecting only one curve.

[X,Y,T,AUC,OPTROCPT,SUBY,SUBYNAMES] = perfcurve(Actual_label,Score,1);

            plot(X,Y)

Here, Actual_label and Score are a cell array of size 5 X 1. Each cell array is of size 70 X 1. And 1 denotes positive class=1.

P.S: I am using One-class SVM and 'fitSVMPosterior' function is not appropriate for one-class learning (same has been mentioned in the documentation of MATLAB). Therefore posterior probability can't be used here.

Answer 1

When you compute the confidence bounds, X and Y are an m-by-3 array, where m is the number of fixed X values or thresholds (T values). The first column of Y contains the mean value. The second and third columns contain the lower bound and the upper bound, respectively, of the pointwise confidence bounds. AUC is also a row vector with three elements, following the same convention.

Above explanation is taken from MATLAB documentation.

Answer 2

That is expected because you are plotting the ROC curve for each of the 5 folds.

Now if you want to have only one ROC for your classifier, you can either use the 5 trained classifiers to predict the labels of an independent test set or you can average the posterior probabilities of the 5 folds and have one ROC.