Heuristic Feature Selection for Gradient Boosting If I am trying to select from two different sets of features for a Gradient Boosting Machine, but I do not want to run through training an entire model on each set, could I differentiate performance with a lower number of trees? 
Suppose based on the other parameters, I need about 1000 trees for the best fit ultimately. If I just want to see if one set of features will probably perform better than another, can I trim the number of trees to 50 and then validate? Or even 5? Does the implementation work in a way that the best trees are chosen early on, and I could assume a lower number of trees might be indicative of ultimate performance or would there be problems at validation? I am using scikit-learn, and I am a bit new, so I just wanted to be sure about how it works.
In short, are early tree fits somewhat indicative of feature importance?
 A: To answer the direct question, the tree position is NOT indicative of importance.
GBM & Random Forest both select the splitting variables randomly & hence early selection is not indicative of feature importance.
You can read the details here
A: One option for you would be to increase the learning rate on your models and fit them all the way (using cross validation to select a optimal tree depth).  This will give you an optimal model with less trees.  Then you can select which set of variables you want based on these two models, and fit an more careful model with a small learning rate on the set you chose.
Keep in mind that you have made a decision, selecting one of two sets of variables, and this will affect the ability of your final model to generalize away from the training data.
A: if you want to change n.trees, don't just directly decrease from 1000 to 5. Instead you should try 995, then see the performance of the model. Also the most important parameters are shrinkage, n.trees and interactio.depth. Generally decreasing shrinkage and increasing n.trees leads to better results.
