Hello list,
I am faced with a two-class classification problem with highly asymetric
class sizes (class one: 99%, class two: 1%).
I'd like to obtain a class probability model, also introducing available
information on the class prior.
Calling kernlab/ksvm with the line
>
ksvm_model1<-ksvm(as.matrix(slides), as.factor(Class), class.weights= c("0"
=99, "1" =1), prob.model=T)
>
or
>
ksvm_model1<-ksvm(as.matrix(slides), as.factor(Class), class.weights=wts,
prob.model=T)
>
with the named vector wts
0 1
99 1
I get the following output:
>
Using automatic sigma estimation (sigest) for RBF or laplace kernel
Error in inherits(x, "factor") : only 0's may be mixed with negative
subscripts
In addition: Warning message:
Variable(s) `' constant. Cannot scale data. in: .local(x, ...)
>
My data is a balanced set of 2500 examples, most of the 65 features are
binary with some real numbers in between.
I am using kernlab in version 0.9-5.
Best regards,
Dominik Gallus
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