Viewing a cross validated classification tree in the

Hi,   I am using the cross validation method 'crossval' as explained in the matlab help;   cvmodel = crossval(model) creates a partitioned model from model, a fitted classification tree. By default, crossval uses 10-fold cross validation on the training data to create cvmodel.   I have already produced the classification tree with M being a matrix of variables and A being a column vector of classification;   tree = ClassificationTree.fit(M, A); and can view this tree using 'view' view(tree,'mode','graph') Previously I had split matrix M into training and sample groups however now I would like to use the cross validation method 'crossval' to produce a more accurate accuracy prediction cvtree = crossval(tree); I can then use kfoldLoss and kfoldPredict to determine the effectiveness of the tree however I cannot determine how to 'view' the tree in any sense. I have attempted the following; view(cvtree) and view(cvtree,'mode','graph') however always get the same error;   *__view(cvtree) Error using classreg.learning.internal.DisallowVectorOps/throwNoCatError (line 57) Concatenation of classreg.learning.partition.ClassificationPartitionedModel objects is not allowed. Use a cell array to contain multiple objects.   Error in classreg.learning.internal.DisallowVectorOps/horzcat (line 48) function a = horzcat(this,varargin), throwNoCatError(this); end   Error in view (line 66) if(~all(isnumeric([viewArgs{:}])))_*   I was of the understanding that the crossval method produces 10 trees each with 90% of the data and tests these on the remaining 10% before selecting the tree which gives the greatest accuracy. Is this correct? and if so, why can I not view this resultant tree?   I really hope someone can help as I have been trawling the internet and matlab documentation for hours with no luck!