[R] Random Forest AUC

[mxkuhn]

I think the issue is that you really can't use the training set to judge this (without resampling). 

For example, k nearest neighbors are not known to over fit, but  a 1nn model will always perfectly predict the training data.

Max

On Oct 23, 2010, at 9:05 AM, "Liaw, Andy" <andy_liaw at merck.com> wrote:

> 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-bounces at r-project.org 
>> [mailto:r-help-bounces at r-project.org] On Behalf Of vioravis
>> Sent: Saturday, October 23, 2010 12:15 AM
>> To: r-help at 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: 
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>> Sent from the R help mailing list archive at Nabble.com.
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