python - Scikit-learn balanced subsampling

Question

I'm trying to create N balanced random subsamples of my large unbalanced dataset. Is there a way to do this simply with scikit-learn / pandas or do I have to implement it myself? Any pointers to code that does this?

These subsamples should be random and can be overlapping as I feed each to separate classifier in a very large ensemble of classifiers.

In Weka there is tool called spreadsubsample, is there equivalent in sklearn? http://wiki.pentaho.com/display/DATAMINING/SpreadSubsample

(I know about weighting but that's not what I'm looking for.)

Answer 1

There now exists a full-blown python package to address imbalanced data. It is available as a sklearn-contrib package at https://github.com/scikit-learn-contrib/imbalanced-learn