SVM question

I'm running SVM from e1071 package on a data with ~150 columns (variables) 
and 50000 lines of data (it takes a bit of time) for radial kernel for 
different gamma and cost values. 

I get a very large models with at least 
30000 vectors and the prediction I get is not the best one. What does it 
mean and what could I do to ameliorate my model ?
Do you mean 30000 *support vectors* in 50000 observations? So you are 
heavily overfitting. Try to tune the svm better.

Uwe Ligges
Jerzy Orlowski

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Well, I know there must be something wrong with the way I'm running the 
SVM but I tried all the posiible ranges of parameters gamma and cost and 
it does not improve? Could You suggest anything?

Georges Orlowski wrote:

I'm running SVM from e1071 package on a data with ~150 columns (variables) 
and 50000 lines of data (it takes a bit of time) for radial kernel for 
different gamma and cost values. 

I get a very large models with at least 
30000 vectors and the prediction I get is not the best one. What does it 
mean and what could I do to ameliorate my model ?
Do you mean 30000 *support vectors* in 50000 observations? So you are 
heavily overfitting. Try to tune the svm better.> 
Uwe Ligges

Jerzy Orlowski

______________________________________________
R-help at stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html