Hi: I can't go into all the details ( Lutz Hamel has a very nice intro book
for SVM's and I wouldn't
do the details justice anyway ) but the objective function in an SVM is
maximizing the margin ( think of the margin as the amount of seperation
between the 2 classes in a 2 class problem ). The objective function
includes a penalty for being wrong when classifying but the "wrongness" is
defined by distance wrong not as a 0-1. So, yes, the objective function is
not minimizing classification rate and the Cost parameter is a penalty for
the how far a point can be in terms of it being on the wrong side of the
hyperplane ( Not a 0-1 type cost ).

I'm not sure what you meant when you said put in a cost of 0.31 but, if you
sent that
param into SVM and kept everything else the same and obtained better
confusion matrix than the one the tuned SVM gives you, I think that's
possible.  Definitely try to get your hands on Lutz's book for a way better
explanation. Or let's hope someone else chimes in.


Mark









On Sun, Aug 19, 2012 at 3:02 PM, delf <aysem_cyp@hotmail.com> wrote:

> Hi everybody,
>
> I am new in e1071 and with SVMs. I am trying to understand the performance
> of SVMs but I face with a situation that I thought as not meaningful.
>
> I added the R code for you to see what I have done.
>
> /set.seed(1234)
> data <- data.frame( rbind(matrix(rnorm(1500, mean = 10, sd = 5),ncol = 10),
> matrix(rnorm(1500, mean = 5, sd = 5),ncol = 10)))
> class <- as.factor(rep(1:2, each=150))
> data<- cbind(data,class)
>
> tuned<-best.svm(class~., data=data, kernel =  "linear", cost =
> seq(0.24,0.44, by = .01), tunecontrol=tune.control(cross=300) )
>
> # test with train data
> predicts <- predict(model, data, probability=TRUE, decision.values = TRUE)
> tab<-table(predicts, data$class)
> tab/
>
> This is what I face:
> /Parameters:
>    SVM-Type:  C-classification
>  SVM-Kernel:  linear
>        cost:  0.26
>       gamma:  0.1
>
> Number of Support Vectors:  61/
>
> But, when I try cost=0.31, I get a lower misclassification error rate than
> when I get with cost=0.26 .
>
> Is this difference because the error used while tuning is different from
> the
> misclassification value?
>
>  Thanks in advance.
>
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/e1071-tuning-is-not-giving-the-best-within-the-range-tp4640747.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

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