Cross Validated Classification Tree Contingency Table

I need the final contingency table for my cross validated classification tree. My code is:

%Create classification tree

tree = ClassificationTree.fit(x,y);

%Cross validate tree

[E,SE,Nleaf,bestlevel] = cvLoss(tree,'subtrees','all','treesize','se','kfold',5);

Which gives me a cross validated tree and E and se. But for validation I need the entire cross validated contingency table so I can compute POD, POFD, FAR.

This is how I understand it: Since it is 5 fold cross validation, 5 trees are created, each built using a random 80% of the data. These trees are then each tested with the corresponding 20% of data to compute a final E and se. What I want are the 5 contingency tables which can be created when the testing data is run through each of the 5 cross validation trees (one from each tree). I then want to sum these 5 tables into an overall final contingency table. Is it possible to access this data so I don't have to code it myself?

Answer 1

[Ilya]

It is not possible to access this data. However, coding is not hard. I presume you want to prune every tree to bestlevel returned by the cvLoss method. Here is how you can compute the overall confusion matrix (contingency table).

load ionosphere
cv = cvpartition(Y,'kfold',5);
Yhat = repmat(Y(1),numel(Y),1);
for k=1:5
    itrain = training(cv,k);
    itest = test(cv,k);
    tree = ClassificationTree.fit(X(itrain,:),Y(itrain));
    tree = prune(tree,'level',bestlevel);
    Yhat(itest) = predict(tree,X(itest,:));
end
confusionmat(Y,Yhat)