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.)