Gradient tree boosting as proposed by Friedman uses decision trees with
J terminal nodes (=leaves) as base learners. There are a number of ways to grow a tree with exactly
J nodes for example one can grow the tree in a depth first fashion or in a breadth first fashion, ...
Is there an established way how to grow trees with exactly
J terminal nodes for gradient tree boosting?
I examined the tree growing procedure of R's
gbm package and it seems that it expands the tree in depth-first fashion and uses a heuristic based on error improvement to choose whether to expand the left or the right child node -- is that correct?