Hello,
I have two questions concerning the RWeka package:
1.) First question:
How can one perform a cross validation, -say 10fold- for a given data
set and given model ?
2.) Second question
What is the correct syntax for the parametrization of e.g. Kernel
classifiers interface
m1 <- SMO(Species ~ ., data = iris, control =
Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=0.1))
m2 <- SMO(Species ~ ., data = iris, control =
Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=1.0))
> m1
SMO
Kernel used:
RBF kernel: K(x,y) = e^-(0.01* <x-y,x-y>^2)
## should be: RBF kernel: K(x,y) = e^-(0.1* <x-y,x-y>^2)
> m2
SMO
Kernel used:
RBF kernel: K(x,y) = e^-(0.01* <x-y,x-y>^2)
## should be: RBF kernel: K(x,y) = e^-(1.0* <x-y,x-y>^2)
That is, the control arguments ignores the parameter 'G' (Gamma) for
the above syntax.
What's wrong with this syntax ?
many thanks
Bjoern
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