matlab predict function error with fitrtree model - matlab

Question

I am trying to do regression with fitrtree model. It works fine without the validation but with the validation the predict function returns an error.
%works fine
tree = fitrtree(trainingData,target,'MinLeafSize',2, 'Leaveout','off');
y_hat = predict(tree, xNew);
%Returns error
tree = fitrtree(trainingData,target,'MinLeafSize',2, 'Leaveout','on');
y_hat = predict(tree, xNew);
Error: Systems of classreg.learning.partition.RegressionPartitionedModel class cannot be used with the "predict"
command. Convert the system to an identified model first, such as by using the "idss" command.
Update: I figured out that when we use cross validation of any sort, the model is in the Trained attribute of tree rather than the tree itself. what is this trained attribute (tree.Trained{1}) and what information do we get from it.?

Answer 1

If you choose a cross-validation method when calling fitrtree(), the output of the function is a RegressionPartitionedModel instead of a RegressionTree.
As you said, you can access objects of type RegressionTree stored in tree.Trained in your case. The number and meaning of the trees you find under this attribute depends on the cross-validation model. In your case, using Leave-one-out cross-validation (LOOCV), the Trained attribute contains N RegressionTree objects, where N is the number of data points in your training set. Each of these regression trees is obtained by training on all but one of your data points. The left out data point is used for testing.
For example, if you want to access the first and last trees obtained from cross-validation, and use them for separate predictions, you can do:
%Returns RegressionPartitionedModel
cv_trees = fitrtree(trainingData,target,'MinLeafSize',2, 'Leaveout','on');
%This is the number of regression trees stored in cv_trees for LOOCV
[N, ~] = size(trainingData);
%Use one of the models from the cross-validation as a predictor
y_hat = predict(tree.Trained{1}, xNew);
y_hat_2 = predict(tree.Trained{N}, xNew);