Dear Eleni,
>
> But if every time you remove a variable you pass some test data (ie data
> not used to train the model) and base the performance of the new, reduced
> model on the error rate on the confusion matrix for the test data, then
> this "overfitting" should not be an issue, right? (unles
Found it from another paper:
importance sample learning ensemble (ISLE)
which originates from Friedman and Popescu (2003).
On 7/31/06, Weiwei Shi <[EMAIL PROTECTED]> wrote:
>
> Hi, Andy:
>
> What's the Jerry Friedman's ISLE? I googled it and did not find the paper
> on it. Could you give me a lin
ndy; r-help@stat.math.ethz.ch
Subject: Re: [R] memory problems when combining randomForests [Broadcast]
Hi, Andy:
What's the Jerry Friedman's ISLE? I googled it and did not find the paper on
it. Could you give me a link, please?
Thanks,
Weiwei
On 7/31/06, Eleni Rapsomaniki <[EMAIL P
Hi Andy,
> > I get different order of importance for my variables depending on their
order in the training data.
Perhaps answering my own question, the change in importance rankings could be
attributed to the fact that before passing my data to randomForest I impute the
missing values randomly (u
Hi, Andy:
What's the Jerry Friedman's ISLE? I googled it and did not find the paper on
it. Could you give me a link, please?
Thanks,
Weiwei
On 7/31/06, Eleni Rapsomaniki <[EMAIL PROTECTED]> wrote:
>
> Hello
>
> I've just realised attachments are not allowed, so the data for the
> example in
> m
Hello
I've just realised attachments are not allowed, so the data for the example in
my previous message is:
pos.df=read.table("http://www.savefile.com/projects3.php?fid=6240314&pid=847249&key=119090";,
header=T)
neg.df=read.table("http://fs07.savefile.com/download.php?pid=847249&fid=9829834&key
Hello again,
The reason why I thought the order at which rows are passed to randomForest
affect the error rate is because I get different results for different ways of
splitting my positive/negative data.
First get the data (attached with this email)
pos.df=read.table("C:/Program Files/R/rw2011/
From: Eleni Rapsomaniki
>
> Hi Andy,
>
> > > I'm using R (windows) version 2.1.1, randomForest version 4.15.
> >^
> > Never seen such a version...
> Ooops! I meant 4.5-15
>
> > > I then save each tree to a file so I can combine
Hi Andy,
> > I'm using R (windows) version 2.1.1, randomForest version 4.15.
>^
> Never seen such a version...
Ooops! I meant 4.5-15
> > I then save each tree to a file so I can combine them all
> > afterwards. There are no memo
From: Eleni Rapsomaniki
>
> I'm using R (windows) version 2.1.1, randomForest version 4.15.
^
Never seen such a version...
> I call randomForest like this:
>
> my.rf=randomForest(x=train.df[,-response_index],
> y=train.df[,respons
I'm using R (windows) version 2.1.1, randomForest version 4.15.
I call randomForest like this:
my.rf=randomForest(x=train.df[,-response_index], y=train.df[,response_index],
xtest=test.df[,-response_index], ytest=test.df[,response_index],
importance=TRUE,proximity=FALSE, keep.forest=TRUE)
(whe
You need to give us more details, like how you call randomForest, versions
of the package and R itself, etc. Also, see if this helps you:
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/32918.html
Andy
From: Eleni Rapsomaniki
>
> Dear all,
>
> I am trying to train a randomForest using all my
Dear all,
I am trying to train a randomForest using all my control data (12,000 cases, ~
20 explanatory variables, 2 classes). Because of memory constraints, I have
split my data into 7 subsets and trained a randomForest for each, hoping that
using combine() afterwards would solve the memory issue
13 matches
Mail list logo
