[R] (package e1071) SVM tune for best parameters: why they are different everytime i run?

From: Maggie Wang <haitian_at_ust.hk>
Date: Thu, 27 Dec 2007 17:20:08 +0800


Hi,

I run the following tuning function for svm. It's very strange that every time i run this function, the best.parameters give different values.

[A]

>svm.tune <- tune(svm, train.x, train.y,

                    validation.x=train.x, validation.y=train.y,

                 ranges = list(gamma = 2^(-1:2),

                 cost = 2^(-3:2)))



# where train.x and train.y are matrix specified.

# output command:

>svm.tune$best.parameters$cost

>svm.tune$best.parameters$gamma

result:

 cost gamma
 0.25 4.00

run A again:

 cost gamma

    1 4

again:

  cost gamma
 0.25 4.00

The result is so unstable, if it varies so much, why do we need to tune? Do you know if this behavior is normal? Can we trust the best.parameters for prediction?

Thank you so much to help out!!

Best Regards,

Maggie

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