In python sklearn, there are multiple algorithms (e.g. regression, random forest ... etc.) that have the class_weight parameter to handle unbalanced data.
However, I do not find such parameter for the MLLib algorithms. Is there a plan of implementing class_weight for some MLLib algorithm? Or is there any approach in MLLib for unbalanced data? Or we actually have to handle all the up/downsampling ourselves in MLLib?