gt; From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Claudia Beleites
> Sent: Saturday, October 23, 2010 3:39 PM
> To: r-help@r-project.org
> Subject: Re: [R] Random Forest AUC
>
> Dear List,
>
> Just curiosity (disclaimer: I never used
orever", 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-
", 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
tate 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, 20
ind 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
&
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
gt; Sent: Friday, October 22, 2010 1:20 AM
> To: r-help@r-project.org
> Subject: [R] Random Forest AUC
>
>
> Guys,
>
> I used Random Forest with a couple of data sets I had to
> predict for binary
> response. In all the cases, the AUC of the training set is
> co
Ravishankar,
> I used Random Forest with a couple of data sets I had to predict for binary
> response. In all the cases, the AUC of the training set is coming to be 1.
> Is this always the case with random forests? Can someone please clarify
> this?
This is pretty typical for this model.
> I hav
Guys,
I used Random Forest with a couple of data sets I had to predict for binary
response. In all the cases, the AUC of the training set is coming to be 1.
Is this always the case with random forests? Can someone please clarify
this?
I have given a simple example, first using logistic regressi
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