[R] how to choose cost in SVM analysis with kernlab
m@rong|u@|u|g| @end|ng |rom gm@||@com
Tue May 7 10:32:43 CEST 2019
I have set a model for SVM analysis using laplacedot with the package
kernlab. I checked the classification error with a k-fold approach,
that is I analyzed 1/10 of the data ten times and averaged the error
(FalseNeg + FalsePos) / TOT. I tested different levels of cost C and
the results are:
Given that the purpose of the optimization is to minimize the error, a
C>=9 is therefore what I am looking for. But, if the model is too
stringent, then I will have problems with the future sets.
So what level should I set? My feeling is that C=1 is enough.
Is there a method within kernlab to maximize C (and gamma perhaps)
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