I have created a decision tree (T) to predict classes, Y, based on features X. I would like to cross-validate the decision tree. When I do so, I can only get the overall cross-validation error, not the vector of classes as they are predicted from the cross-validation.
Here is the code I am trying to use: T3 = classregtree(X,Y,'method','classification'; view(T3); cp = cvpartition(Y,'k',10);
dtClassFun = @(xtrain,ytrain,xtest)(eval(classregtree(xtrain,ytrain),xtest));
crossvalerrorrT = crossval('mcr',X,Y,'predfun',dtClassFun,'partition',cp)
This will output a scalar overall cross-validation error.
How can I output a vector of cross-validation predicted classes (so that I can look at cross-validation error for each class separately)?
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