R help - Apr 2009 - Running random forest using different training and testing schemes

Hi,

I would like to run random Forest classification algorithm and check the
accuracy of the prediction according to different training and testing
schemes. For example, extracting 70% of the samples for training and the
rest for testing, or using 10-fold cross validation scheme.
How can I do that? Is there a function?

Thanks a lot,

Chrysanthi.

	[[alternative HTML version deleted]]

Hi Chysanthi,

check out the randomForest package, with the function randomForest. It has a CV
option. Sorry for not providing you with a lengthier response at the moment but
I'm rather busy on a project. Let me know if you need more help.

Also, to split your data into two parts- the training and the test set you can
do (n the number of data points):
n<-length(data[,1])
indices<-sample(rep(c(TRUE,FALSE),each=n/2),round(n/2),replace=TRUE)
training_indices<-(1:n)[indices]
test_indices<-(1:n)[!indices]

Then, data[train,] is the training set and data[test,] is the test set.

Best,
Pierre


________________________________
De : Chrysanthi A. <chrysain@gmail.com>
À : r-help@r-project.org
Envoyé le : Dimanche, 12 Avril 2009, 17h26mn 59s
Objet : [R] Running random forest using different training and testing schemes

Hi,

I would like to run random Forest classification algorithm and check the
accuracy of the prediction according to different training and testing
schemes. For example, extracting 70% of the samples for training and the
rest for testing, or using 10-fold cross validation scheme.
How can I do that? Is there a function?

Thanks a lot,

Chrysanthi.

    [[alternative HTML version deleted]]

______________________________________________
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.



      
	[[alternative HTML version deleted]]

you need to include in your code something like:

tree<-rpart(result~., data, control=rpart.control(xval=10)).

this xval=10 is 10-fold CV.

Best,
Pierre


________________________________
De : Chrysanthi A. <chrysain@gmail.com>
À : r-help@r-project.org
Envoyé le : Dimanche, 12 Avril 2009, 17h26mn 59s
Objet : [R] Running random forest using different training and testing schemes

Hi,

I would like to run random Forest classification algorithm and check the
accuracy of the prediction according to different training and testing
schemes. For example, extracting 70% of the samples for training and the
rest for testing, or using 10-fold cross validation scheme.
How can I do that? Is there a function?

Thanks a lot,

Chrysanthi.

    [[alternative HTML version deleted]]

______________________________________________
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.



      
	[[alternative HTML version deleted]]

There is also the train function in the caret package. The  
trainControl function can be used to try different resampling schemes.  
There is also a package vignette with details.

Max


On Apr 12, 2009, at 12:26 PM, "Chrysanthi A." <chrysain at
gmail.com>
wrote:
> Hi,
>
> I would like to run random Forest classification algorithm and check  
> the
> accuracy of the prediction according to different training and testing
> schemes. For example, extracting 70% of the samples for training and  
> the
> rest for testing, or using 10-fold cross validation scheme.
> How can I do that? Is there a function?
>
> Thanks a lot,
>
> Chrysanthi.
>
>    [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at 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.