Hi Sergey,
There is a sample_weights option (not very well documented) in the random
forest classifier that might help. You might want to check out the SVC example
to see the sample_weights format.
http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html
You can provide different weights to different classes (for e.g., inversely
proportional to the number of samples).
-Manish
On Jul 12, 2013, at 4:40 PM, Sergey Feldman <sergeyfeld...@gmail.com> wrote:
> I'm dealing with a 50-class classification problem with extremely unbalanced
> classes. The smallest class has about 1000 samples and the largest has
> 500,000. The random forest I've trained is being heavily skewed towards the
> big classes.
>
> Is there a good way to deal with this kind of problem in sklearn as of now?
> Or is there room to implement some kind of stratified bootstrap strategy or a
> weighting strategy (as in here, for example)?
>
> What other non-linear classifiers in sklearn would be good for this kind of
> dataset? It's about 2 million examples in 500+ dimensions.
>
> Thanks,
> Sergey
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