Hi,
I have a question about the parameter C (cost) in svm function in e1071. I
thought larger C is prone to overfitting than smaller C, and hence leads to
more support vectors. However, using the Wisconsin breast cancer example on
the link:
http://planatscher.net/svmtut/svmtut.html
I found that the largest cost have fewest support vectors, which is contrary
to what I think. please see the scripts below:
Am I misunderstanding something here?
Thanks a bunch,
-Jack
> model1 <- svm(databctrain, classesbctrain, kernel = "linear", cost = 0.01)
> model2 <- svm(databctrain, classesbctrain, kernel = "linear", cost = 1)
> model3 <- svm(databctrain, classesbctrain, kernel = "linear", cost = 100)
> model1
Call:
svm.default(x = databctrain, y = classesbctrain, kernel = "linear",
cost = 0.01)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 0.01
gamma: 0.1111111
Number of Support Vectors: 99
> model2
Call:
svm.default(x = databctrain, y = classesbctrain, kernel = "linear",
cost = 1)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 1
gamma: 0.1111111
Number of Support Vectors: 46
> model3
Call:
svm.default(x = databctrain, y = classesbctrain, kernel = "linear",
cost = 100)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 100
gamma: 0.1111111
Number of Support Vectors: 44
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