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')
John Michell answered . 2024-05-17 23:17:03
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.
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