Hi all,
I wrote a couple of weeks ago about implementing the Boruta all-relevant
feature selection method algorithm in Python..
I think it's ready to go now. I wrote fit, transform and fit_transform
methods for it to make it sklearn like.
Here it is:
https://bitbucket.org/danielhomola/boruta_py
Let me know what you think. If anyone thinks this might be worthy of
adding it to the feature selection module, the original author Miron is
happy to give his blessing, and I'm happy work on it further.
Cheers,
Daniel
On 15/04/15 11:03, Daniel Homola wrote:
Hi all,
I needed a multivariate feature selection method for my work. As I'm
working with biological/medical data, where n < p or even n << p I
started to read up on Random Forest based methods, as in my limited
understanding RF copes pretty well with this suboptimal situation.
I came across an R package called Boruta:https://m2.icm.edu.pl/boruta/
<https://exchange.imperial.ac.uk/owa/redir.aspx?C=Yp1dHGp6hkyiZQZzx17DHznOv7PxStIIK3PgwAs_McazihitoU3Fm6_EBXvwfIJB2CJSzkCKKjo.&URL=https%3a%2f%2fm2.icm.edu.pl%2fboruta%2f>
After reading the paper and checking some of the pretty impressive
citations I thought I'd try it, but it was really slow. So I thought
I'll reimplement it in Python, because I hoped (based on
thishttp://www.slideshare.net/glouppe/accelerating-random-forests-in-scikitlearn
<https://exchange.imperial.ac.uk/owa/redir.aspx?C=Yp1dHGp6hkyiZQZzx17DHznOv7PxStIIK3PgwAs_McazihitoU3Fm6_EBXvwfIJB2CJSzkCKKjo.&URL=http%3a%2f%2fwww.slideshare.net%2fglouppe%2faccelerating-random-forests-in-scikitlearn>)
that it will be faster. And it is :) I mean a LOT faster..
I was wondering if this would be something that you would consider
incorporating into the feature selection module of scikit-learn?
If yes, do you have a tutorial or some sort of guidance about how
should I prepare the code, what conventions should I follow, etc?
Cheers,
Daniel Homola
STRATiGRAD PhD Programme
Imperial College London
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