Hi all, I apologize - i've been looking for this answer all over the internet, and it could be that I'm not googling the right terms.
For managing unbalanced datasets, Weka has SMOTE, and scikit has randomoversampling. In weka, we can ask it to boost by a given percentage (say 100%) so an undersampled class with 10 values ends up with 20 values (100% increase) after boosting. In Scikit learn, I cant seem to find a way to do this. The ramdomoversampler boosts arbitrarily. and seem to try to balance the two classes, which may not be realistic in some cases. Can anyone point me to how I can manage boosting percentage using scikit? -- Best Regards, Suranga
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