"Important factors for prediction": how do you work?
I'm using Random Forest and Boosted Trees from AutoModel to prioritize the variables I'll use in modeling with neuralnetworks. So, for me, it's very important. So, for me, it is essential to know the "importance" of each dependent variable. As a result, AutoModel provides "Important factors for prediction", but I don't no how its works. I think is based in correlation but, in this case, should be independent of the type of modeling, but for Random Forest and Boosted Trees different results are generated. And more, before and after optimization, different results are generated to.
My question is: how is the importance of factors calculated?