
 

Hi ,
Could you please help me in this question:?
After trying this code:
 
library(e1071)
 mydata <- as.matrix(read.delim("iris.txt"))
 train.x <- mydata[,-1]
 train.y <- mydata[,1]
 mymodel <- svm(train.x, train.y, cross=3, type="C-classification")
 
I receive this error:
 
Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric
 
I put the class label in the first column.

Cheers,
Amy
 

> Date: Wed, 3 Feb 2010 10:59:27 -0500
> Subject: Re: [R] svm
> From: mailinglist.honeypot@gmail.com
> To: amy_4_5_84@hotmail.com
> CC: r-help@r-project.org
> 
> HI Amy,
> 
> On Wed, Feb 3, 2010 at 1:56 AM, Amy Hessen <amy_4_5_84@hotmail.com> wrote:
> >
> > Hi Steve,
> >
> > Could you please help me in this point?:
> >
> > I use SVM of R and I’m trying some datasets from UCI but when I compare the
> > results of my program( that does not do anything more than calling SVM) with
> > the RMSE of SVM in any other paper, I found a big gap between them.
> >
> > For example, this is the rmse of svm of my program for the dataset bodyfat:
> > 2.64561
> >
> > And this is the RMSE of a paper 0.0204.
> >
> > Could you please tell me how I can reduce this gap in the performance of
> > SVM?
> 
> Sorry, it's hard to say w/o investing any real time to investigate
> (and I unfortunately don't have the time to do so).
> 
> There are different parameters you can play with in nu-regression vs.
> eps-regression and different kernel functions that can be used that
> might be a better fit for the type of data you are trying to learn
> against.
> 
> Before running the SVM (or any other "learning" alogorithm), there are
> also ways to normalize your data, too ..
> 
> Lots of things to look at ...
> 
> -steve
> 
> -- 
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
> | Memorial Sloan-Kettering Cancer Center
> | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
 		 	   		  
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