Hi, Uwe,

Thanks for the reply!!  I have 87 observations in total. If this amount
causes the different best.parameters, is there a better way than cross
validation to tune them?

Thank you so much for the help!

Best Regards,
Maggie

On Dec 27, 2007 6:17 PM, Uwe Ligges <ligges@statistik.uni-dortmund.de>
wrote:

>
>
> Maggie Wang wrote:
> > Hi,
> >
> > I run the following tuning function for svm. It's very strange that
> every
> > time i run this function, the best.parameters give different values.
> >
> > [A]
> >
> >> svm.tune <- tune(svm, train.x, train.y,
> >
> >                     validation.x=train.x, validation.y=train.y,
> >
> >                  ranges = list(gamma = 2^(-1:2),
> >
> >                  cost = 2^(-3:2)))
> >
> >
> >
> > # where train.x and train.y are matrix specified.
> >
> >
> >
> > # output command:
> >
> >
> >
> >> svm.tune$best.parameters$cost
> >
> >> svm.tune$best.parameters$gamma
> >
> >
> >
> > result:
> >
> >  cost gamma
> >  0.25  4.00
> >
> >
> >
> > run A again:
> >
> >  cost gamma
> >     1     4
> >
> >
> >
> > again:
> >
> >   cost gamma
> >  0.25  4.00
> >
> >
> >
> > The result is so unstable, if it varies so much, why do we need to tune?
> Do
> > you know if this behavior is normal? Can we trust the best.parametersfor
> > prediction?
>
> I guess you do not have really many observations in your dataset. Then
> it highly depends ion the cross validation sets which parameter is best.
> And therefore you get quite different results.
>
> Uwe Ligges
>
>
>
> >
> >
> > Thank you so much to help out!!
> >
> >
> >
> > Best Regards,
> >
> > Maggie
> >
> >       [[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<http://www.r-project.org/posting-guide.html>
> > and provide commented, minimal, self-contained, reproducible code.
>

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