Fellow sklearners,
I am working on a classification problem with an unbalanced data set and
have been successful using SVM classifiers with the class_weight option.
I have also tried Random Forests and am getting a decent ROC performance
but I am hoping to get a performance improvement by using Weighted or
Balanced Random Forests as suggested in this paper.
http://www.stat.berkeley.edu/tech-reports/666.pdf
I don't see any implementation of these options but I might be mistaken so
I wanted to ask the community. Also, I am willing to write code and
contribute back if this will be useful to other folks.
I have also thought about balancing the data using up/down sampling the
minority/majority class (with or without replacement) and even SMOTE but
couldn't find those implementation in the scikit-learn library yet. The
modified Random Forests seem to outperform these methods according to the
paper, hence I am interested in trying those first.
-Manish
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