related to http://stats.stackexchange.com/questions/174229/feature-importance-for-random-forest-classification-of-a-sample

This [blog][1] by Ando Saabas suggests a nice way to interpret a tree result for a specific sample into per-feature contributions. Basically he goes over the path to the sample and counts each delta in the prediction $pred(currentNode) - pred(parentNode)$ as a contribution for the splitting feature.

Can I use a similar approach for interpreting results of gradient boosting with tree weak classifiers, specifically for the popular `xgboost` implementation? 

  [1]: http://blog.datadive.net/interpreting-random-forests/