[BioC] How to do k-fold validation using SVM
Stephen Henderson
s.henderson@ucl.ac.uk
Fri, 24 Jan 2003 13:26:07 -0000
No not before you start but after each fold, so that each training round
uses a slightly different set of genes/features.
-----Original Message-----
From: Robert Gentleman [mailto:rgentlem@jimmy.harvard.edu]
Sent: Friday, January 24, 2003 1:16 PM
To: Stephen Henderson
Subject: Re: [BioC] How to do k-fold validation using SVM
On Fri, Jan 24, 2003 at 01:08:23PM -0000, Stephen Henderson wrote:
> Is there a simple??? way to do a gene/feature selection for each round of
> cross validation-- using the ipred errorest function?
>
do you mean take a subset before you start? there is a whole package
called genefilter that does all sorts of things in that regard?
robert
> I do not mean select some set of genes and then do a cv on this subset,
but
> rather to reselect the subset for each fold?
>
> I had written a rather long winded loop previous to this posting (had
missed
> ipred) but now wonder if there is a shortcut?
>
> -----Original Message-----
> From: Torsten Hothorn [mailto:Torsten.Hothorn@rzmail.uni-erlangen.de]
> Sent: Friday, January 24, 2003 7:13 AM
> To: Adaikalavan Ramasamy
> Cc: Song, Guangchun; bioconductor@stat.math.ethz.ch
> Subject: RE: [BioC] How to do k-fold validation using SVM
>
> On Fri, 24 Jan 2003, Adaikalavan Ramasamy wrote:
>
> > You might want to use the function svm() in the e1071 library with the
> > option 'cross'.
> >
> > Or you can manually break the dataset into k subsets and write a loop.
> > This might be better if you prefer to do stratified sampling for the
> > fold rather than random sampling.
> >
>
> or you can use the "errorest" function in the ipred-package (see R News
> 2(2) for examples)
>
> Torsten
>
> > -----Original Message-----
> > From: Song, Guangchun [mailto:Guangchun.Song@stjude.org]
> > Sent: Friday, January 24, 2003 7:35 AM
> > To: bioconductor@stat.math.ethz.ch
> > Subject: [BioC] How to do k-fold validation using SVM
> >
> >
> >
> > Did anyone know how to do the k-fold validation on the training data set
> > by SVM?
> >
> > Thanks.
> >
> >
> > Guangchun
> >
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> >
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> >
> >
>
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