Do you suggest to average the decision functions of all estimators and then use it for making the final predictions? Is it actually a sensible thing to do? Since decisions are unbounded, I fear some may overdominate the others (in particular when `max_features < 1.0`, which may lead to very different decision functions), while they should equally contribute. I have never ensembled such models, nor read about that in the literature, so I can't say for sure. 

Right now, the implementation works for SVC, even if `proba=False`. In the later case, it simply resorts to voting, as the actual bagging algorithm was originally defined.