@rfeigel … or just use XGBoost and be done 
on Mac and Linux Python H2O AutoML will include that in the models tested.
One can also try and combine that with some hyper parameter optimization:
In a lot of cases it will not be a question of 0.84 vs 0.85 which might shift some anyway but to think about what to do and where to make the cut for a prescribed action and also integrate some cost estimations.