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From deneche abdelhakim <a_dene...@yahoo.fr>
Subject Re: [gsoc] random forests
Date Sat, 21 Mar 2009 11:46:34 GMT

just finished reading the 2008 version of the paper and, as you said, it indeed contains very
useful testing approaches, although a big part of it is related to regression and not classification,
but random forests can be used for regression too (regression forests).

BART seems powerful, but I wonder if it is not too difficult to implement comparing to Random
Forests ? I'm saying this because I can't find a no-nose-bleeding algorithm (without a ton
of mathematical formulas).

--- En date de : Lun 16.3.09, Ted Dunning <ted.dunning@gmail.com> a écrit :

> De: Ted Dunning <ted.dunning@gmail.com>
> Objet: Re: [gsoc] random forests
> À: mahout-dev@lucene.apache.org
> Date: Lundi 16 Mars 2009, 7h23
> Here is an interesting related paper
> that gives some good pointers for
> testing (and an alternative related approach)
> 
> http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/*BART*
> %206--06.pdf<http://www-stat.wharton.upenn.edu/%7Eedgeorge/Research_papers/BART%206--06.pdf>
> 
> or
> 
> http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/*BART*
> %20June%2008.pdf<http://www-stat.wharton.upenn.edu/%7Eedgeorge/Research_papers/BART%20June%2008.pdf>
> 
> (these seem to be versions of the same paper).
> 
> On Sun, Mar 15, 2009 at 1:53 AM, deneche abdelhakim <a_deneche@yahoo.fr>wrote:
> 
> >
> > I added a page to the wiki that describes how to build
> a random forest and
> > how to use it to classify new cases.
> >
> > http://cwiki.apache.org/confluence/display/MAHOUT/Random+Forests
> >
> >
> >
> >
> >
> >
> 
> 
> -- 
> Ted Dunning, CTO
> DeepDyve
> 
> 111 West Evelyn Ave. Ste. 202
> Sunnyvale, CA 94086
> www.deepdyve.com
> 408-773-0110 ext. 738
> 858-414-0013 (m)
> 408-773-0220 (fax)
> 


      

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