# How to conduct Support Vector Mache using R(e1071)

Could anyone help me to interpret these results from R? How can I find nBSV,nSv, obj and iteration numbers? How can I use nBSV and nSv, obj to determine which kernel is the best?

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> library(e1071)
> data(cats, package="MASS")
> inputData <- data.frame(cats[, c (2,3)], response as.factor(cats\$Sex))
> svmfit <- svm(cross=10,response ~ ., data = inputData, kernel = "linear",
cost = 10, scale = FALSE)
> print(svmfit)
Call:
svm(formula = response ~ ., data = inputData, cross = 10, kernel = "linear",
cost = 10, scale = FALSE)

Parameters:
SVM-Type:  C-classification
SVM-Kernel:  linear
cost:  10
gamma:  0.5

Number of Support Vectors:  79
> plot(svmfit, inputData)


• For whatever it's worth, I voted to keep this open. It uses R code as an example, but the underlying question (what do the # of support vectors, etc tell you about the kernel?) is very on-topic... Dec 16 '15 at 17:56
• As @MattKrause says, 'how to interpret these results?' is on topic here. However, I don't see the results. Can you paste them in? Can you include the plot? Dec 16 '15 at 19:04

The number of support vectors (both bounded and free) are poor quality indicators of a model. A large amount of support vectors can indicate overfitting, but this is not generally true. The final cost (obj) is also entirely useless for that matter.