Hi all -

 

(Re my previous question to the list, I managed to generate an ROC plot
for my SVM by ranking the data using the Decision.Values property.
Thanks for your responses)

 

I have now started tuning the SVM to get optimal parameters for the RBF
kernel and I ran into a problem. Whatever parameter ranges I specify, I
always get the same error values for all combinations of parameters it
tries. How is "Error" defined in tune.svm? How can I compare this to
results of a single point calculation? Error seems roughly 1- accuracy,
but not exactly.

 

Thanks for any help.

 

Ton

 

=== tuning

> t <- tune(svm, x,y, ranges = list(gamma = 2^(-1:1), cost = 2^3),
control = tune.control(sampling = "cross", performances=TRUE))

> summary(t)

 

Parameter tuning of `svm':

 

- sampling method: 10-fold cross validation 

 

- best parameters:

 gamma cost

   0.5    8

 

- best performance: 0.1120897 

 

- Detailed performance results:

  gamma cost     error

1   0.5    8 0.1120897

2   1.0    8 0.1120897

3   2.0    8 0.1120897

 

=== Single point run

> t <- svm(x,y, cross = 10, gamma = 2^0, cost = 2^3, fitted=TRUE)

> summary(t)

 

Call:

 svm.default(x = x, y = y, gamma = 2^0, cost = 2^3, cross = 10,
fitted = TRUE) 

 

Parameters:

   SVM-Type:  C-classification 

 SVM-Kernel:  radial 

       cost:  8 

      gamma:  1 

 

Number of Support Vectors:  788

 

 ( 88 700 )

 

 

Number of Classes:  2 

 

Levels: 

 false true

 

10-fold cross-validation on training data:

 

Total Accuracy: 88.91626 

Single Accuracies:

 82.71605 86.41975 91.35802 91.35802 95.12195 97.53086 90.12346 88.88889
91.35802 74.39024


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