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!
John Michell answered .
2025-11-20
when you crossvalidate, the output is a ClassificationPartitionedModel, which means that it contains all the cross-validated trees and will use all of them to do prediction. All the individual trees can be accessed as follows:
>> cvtree.Trained
>> view(cvtree.Trained{1},'mode','graph')
>> view(cvtree.Trained{2},'mode','graph') % etc
All the individual losses (of course based on your loss function) can be accessed as below:
>> cvtree.kfoldLoss('mode','individual')
You can view and use an individual tree if you like. Use 'kfoldPredict' to predict from the entire crossvalidated model.