What Breiman meant is that as the model gets more complex (i.e., as the number of trees tends to infinity) the geneeralization error (test set error) does not increase. This does not hold for boosting, for example; i.e., you can't "boost forever", which nececitate the need to find the optimal number of iterations. You don't need that with RF.
> -----Original Message----- > From: r-help-boun...@r-project.org > [mailto:r-help-boun...@r-project.org] On Behalf Of vioravis > Sent: Saturday, October 23, 2010 12:15 AM > To: r-help@r-project.org > Subject: Re: [R] Random Forest AUC > > > Thanks Max and Andy. If the Random Forest is always giving an > AUC of 1, isn't > it over fitting??? If not, how do you differentiate this from over > fitting??? I believe Random forests are claimed to never over > fit (from the > following link). > > http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.ht > m#features > > > Ravishankar R > -- > View this message in context: > http://r.789695.n4.nabble.com/Random-Forest-AUC-tp3006649p3008157.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > Notice: This e-mail message, together with any attachme...{{dropped:11}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
