On 19.02.2010 01:29, Amy Hessen wrote:
Hi
Thank you very much for your reply.
I meant I got different RMSE with different runs to the same program without
any change for the dataset or the parameters of SVM.
This is my code:
library(e1071)
readingmydata<- as.matrix(read.delim("mydataset.txt"))
train.x<- readingmydata[,-1]
train.y<- readingmydata[,1]
mymodel<- svm(train.x, train.y, cross=10)
summary(mymodel)
>
can you please tell me how I can fix that error?
No error at all, it is expected: You get different partitions of the
data for cross validation since they are samples "at random". If you
want to get the exactly same results, use a seed for the random number
generator such as:
set.seed(123)
mymodel<- svm(train.x, train.y, cross=10)
summary(mymodel)
Uwe Ligges
Cheers,
Amy
Date: Tue, 16 Feb 2010 10:33:19 -0500
Subject: Re: [R] svm and RMSE
From: mailinglist.honey...@gmail.com
To: amy_4_5...@hotmail.com
CC: r-help@r-project.org
Hi,
On Fri, Feb 12, 2010 at 3:00 PM, Amy Hessen<amy_4_5...@hotmail.com> wrote:
Hi,
Every time I run a svm regression program, I got different RMSE value.
Could you please tell me what the reason for that?
Sorry, your question is a bit vague.
Can you provide an example/code that shows this behavior? Is the
different RMSE over different folds of cross validation. Over the same
data? With the same parameters? Is the RMSE significantly different?
Providing an example that shows this behavior would help.
Thanks,
-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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