# Getting information from a GBM model

I built a gbm model (using caret package) in order to predict the probability of someone buy or not a car. However as this kind of model act as a black box my issue is to replicate the "profile" of the group who has higher probability to buy. There is any way to discover the values of the predictors that generate high prob?

• You may be using sledgehammer where a normal hammer may do better for your problem domain. Is extreme predictive accuracy actually important for your application, or just good enough accuracy? You may want to consider using a good decision tree algorithm alone, as it is easy to derive the kind of information you are after from a tree. – Matthew Drury Jul 13 '16 at 16:27
• Actually, based on my data this is a kind of a "hard" problem to solve with the "regulars" algorithms, this kind of algortihms(trees,logreg...) didn't give me a "good" accuracy and because of this I perfomed the gbm, and I can't figure out a simple way to discover this profiles – Michael Elma Jul 13 '16 at 16:34
• That's kind of the fundamental trade off. If you want to get better accuracy, you have to use a model that can assume a more complex shape so it can fit the data. But by assuming a more complex shape, it becomes drastically more difficult to meaningfully derive simple information from it. Also, if you need a complex algorithm to get good predictive power, that goes some way to saying that there may be no simple profile that is meaningful at all. – Matthew Drury Jul 13 '16 at 16:35

Assuming you're using R, the function is relative.influence(..). But you don't need it because summary(mod) would give you the same thing.