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 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.

--

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