I'm wondering if tree based methods are capable of making predictions that are larger in magnitude than the largest training observations? Given my understanding of decision trees and partitioning feature space, I would guess it's not possible for predictions from GBMs or Random Forests to exceed the largest values found in training data.
Assuming the above is true, how do modelers deal with these tree-based limitations. If tree based algorithm predictions are bounded by what's observed in training data, I would imagine this would result in prediction problems for applications where unprecedented values are feasible. Other than employ a different algorithm, is there a different way to handle this boundedness of predictions?
