Stratified sampling for Random forest -Python
You can use parameter class_weight .
Weights associated with classes in the form {class_label: weight}
You can give more weight to your small class and find best weight using cross-validation.
For example class_weight={1: 10, 0:1}. Gives more weight to class labeled 1.
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?