Using the sample_weight parameter in the RandomForestClassifier along with the 
balance_weights method from the preprocessing module to generate the sample 
weights might work as well. 

You can check this link for a previous related discussion.
http://sourceforge.net/mailarchive/message.php?msg_id=30460177

-Manish

On Feb 25, 2013, at 5:09 AM, Philipp Singer <kill...@gmail.com> wrote:

> Hey!
> 
> One simple solution that often works wonders is to set the class_weight 
> parameter of a classifier (if available) to 'auto' [1].
> 
> If you have enough data, it often also makes sense to balance the data 
> beforehand.
> 
> [1] http://scikit-learn.org/dev/modules/svm.html#unbalanced-problems
> 
> Am 25.02.2013 14:02, schrieb Maor Hornstein:
>> I'm using scikit-learn in my Python program in order to perform some
>> machine-learning operations. The problem is that my data-set has severe
>> imbalance issues.
>> 
>> Does anyone know a solution for imbalance in scikit-learn or in python
>> in general?
>> 
>> 
>> Thanks :)
>> 
>> 
>> 
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