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function criterion = featureTest(Xtrain, Ytrain, Xtest, Ytest)
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t = ClassificationTree.fit(Xtrain,Ytrain);
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Y_t = t.predict(Xtest);
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Cmat = confusionmat(Ytest,Y_t);
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% Confusion matrix in percentage/100
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Cmat = bsxfun(@rdivide,Cmat,sum(Cmat,2));
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% Misclassification rate for each class
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misclassification = 1 - diag(Cmat);
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criterion = sum(misclassification);
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%criterion = Cmat(1,2)/sum(Cmat(:,2));
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end
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