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

	[[alternative HTML version deleted]]

