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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