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?