tuning svm parameters in R (linear SVM kernel)

Question

what is the difference between tune.svm() and best.svm().

When we tune the parameters of svm kernel, aren't we expected to always choose the best values for our model.

Pardon as i am new to R and machine learning.

I noticed that there was no linear kernel option in tuning svm. Is there a possibility to tune my svm using a linear kernel

Solution

From ETHZ: best.svm() is really just a wrapper for tune.svm(...)$best.model. The help page for tune() will tell you more on the available options.

Be sure to also go through the examples on the help page for tune(). e1071::svm offers linear, radial (the default), sigmoid and polynomial kernels, see help(svm). For example, to use the linear kernel the function call has to include the argument kernel = 'linear':

data(iris)
obj <- tune.svm(Species~., data = iris, 
                cost = 2^(2:8), 
                kernel = "linear") 

If you are new to R and would like to train and cross validate SVM models you could also check the caret package and its train function which offers multiple types of kernels. The whole 'topics' section on that site might be of interest, too.

Comments

[aceminer]

I checked and theres no parameter for selection of kernel. It threw up an error for me

[thie1e]

Which function threw the error? As in the example above, the kernel selection works in tune.svm.

[aceminer]

think it was a typo. Its working fine now thanks a lot.