I have a simple gradient boosting model (maximum branch = 2 , maximum depth = 1 {Adaboost} ) in e-miner(v 14.1) with binary target and mostly interval inputs(~500 variables). I will be choosing variables if the variable importance > 0.05 for both training and validation datasets. However, I am trying to understand the mathematics behind how the "variable importance" is calculated. I read the documentation (decision tree variable importance ) but its very vague. I was wondering if anyone could shed light on how it is calculated with a simple example? It will be very helpful.  ... View more