Stratified sampling for Random forest -Python
In newer versions of sklearn's random forest classifier, you can simply set class_weight="balanced".
I'm building a random forest classification model with the response variable split being 98%(False)-2%(True). I'm using Scikit Learn's RandomForest classifier for this. 
What is the best way to handle this unbalanced data and avoid oversampling?