GBM R function: get variable importance separately for each class

Question

I am using the gbm function in R (gbm package) to fit stochastic gradient boosting models for multiclass classification. I am simply trying to obtain the importance of each predictor separately for each class, like in this picture from the Hastie book (the Elements of Statistical Learning) (p. 382).

However, the function summary.gbm only returns the overall importance of the predictors (their importance averaged over all classes).

Does anyone know how to get the relative importance values?

Answer 1

I think the short answer is that on page 379, Hastie mentions that he uses MART, which appears to only be available for Splus.

I agree that the gbm package doesn't seem to allow for seeing the separate relative influence. If that's something you're interested in for a mutliclass problem, you could probably get something pretty similar by building a one-vs-all gbm for each of your classes and then getting the importance measures from each of those models.

So say your classes are a, b, c, & d. You model a vs. the rest and get the importance from that model. Then you model b vs. the rest and get the importance from that model. Etc.