Well actually, i'm able to answer this myself. Its the ratio attribute (see: http://contrib.scikit-learn.org/imbalanced-learn/generated/imblearn.over_sampling.RandomOverSampler.html )
:) :) On Tue, Jan 10, 2017 at 12:36 PM, Suranga Kasthurirathne < suranga...@gmail.com> wrote: > > 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 > -- Best Regards, Suranga
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