Statistics 4240 - Data Mining

Is it reasonable to evaluate the predictive performance of bagged regression trees using cross validation? For example, for each fold, you create B bootstrap samples from the training data (non hold out data), fit a regression tree to each one, then take the average of the B predictions on the hold-out data (call this the bagged predictions). Do this for each fold then take the average of the squared difference between bagged predictions for each fold and the actual values to come up with your estimate of predictive accuracy. Does anyone ever do this?

Yes, this is certainly something people do, although its obviously very expensive.