[scikit-learn] Specify boosting percentage using Randomoversampling?

[Suranga Kasthurirathne]

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 <
surangakas at 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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