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!
Fit Decision Tree to Gradient Boosted Trees for Interpretability
python;classification;decision trees;gbm