How to best do cross-validation using fitensemble?

Tobias Pahlberg

2016 6 23

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I generated code from the Classification Learner app where I wanted to cross-validate a classifier. It gave me something like:

 classificationEnsemble = fitensemble(predictors, response, Bag', nRounds, Tree', type', 'Classification', ...); 
 partitionedModel = crossval(classificationEnsemble, 'KFold', 5);

But I can also provide the cross-validation parameter directly into the fitensemble command. Like:

classificationEnsemble = fitensemble(..., 'KFold', 5);

Is there any difference here? Is the first case trained on all the data and then cross-validated??

/Thanks

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Don Mathis 2017-3-31

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It doesn't make any difference in the models. The only difference is that with the first method you also get a single model trained on the full dataset.