I was wondering if there is literature on or someone could explain how to fit a decision tree to a gradient boosted trees classifier in order to derive more interpretable results.
This is apparently the approach that Turi uses in their explain function which outputs something like this:
from their page here.
I know that for random forests you can average the contribution of each feature in every tree as seen in the TreeInterpreter python package, but this seems to be a different method, since it is focused on exact splits and one decision tree.
Does anyone know anything more about this method for interpreting gradient boosted trees? Thanks!