MATLAB decision tree classregtree both classification and regresstion

matrixTrain = load('primate.train' );
featureInTrain = matrixTrain( :, 1:end-1);
featureOutTrain = matrixTrain(:,end);

matrixTest = load('primate.test' );
featureInTest = matrixTest( :, 1:end-1);
featureOutTest = matrixTest(:,end);


% tree
% t = classregtree(featureInTrain,featureOutTrain,'method','classification');
% predictedOut =str2double( eval(t,featureInTest))

%tree bagger
bnew = TreeBagger(10 ,featureInTrain , featureOutTrain, 'Method','classification') % for 10 tree
predictedOut = predict(bnew, featureInTest)
predictedOut = str2double(predictedOut)


t = bnew.Trees{1,1}
t =bnew.Trees{1,2}
t =bnew.Trees{1,3}
... ... ...
t =bnew.Trees{1,10}