# Gradient boosting in R uses only a single variable

I am trying to build a boosting model using the package gbm in R. I have the following code:

gb = gbm(aaa_target ~ .,
data=myDdata,
n.trees=100,
verbose=TRUE)


and when I have trained the model, I can get a summary like this:

summary(gb)


The issue I am having, is that only a single variable (out of around 30) is selected and is given 100% predictive power. I know for a fact that many of the variables carry information (although the selected one is the most significant one), and using the randomForest package gives me a model which assigns significance to many of the variables.

Does anybody have a clue to why this might be the case?

Because the overworked maintainers of the gbm package have not had time to implement random feature sampling at each split calculation yet. I submitted a bad patch that did this as a proof of concept, but: