Hi
 
I am trying to fit an svm to predict  speech recognition errors.  I am
using best.svm like this:
 
svm.model = best.svm(data[1:3000,1:23],data[1:3000,24],tunecontrol =
tune.control())
 
I got this:
 
> print(svm.model)
 
Call:
 best.svm(x = data[1:3000, 1:23], tunecontrol = tune.control(),
data[1:3000, 24]) 
 
Parameters:
   SVM-Type:  eps-regression 
 SVM-Kernel:  radial 
       cost:  1 
      gamma:  0.04347826 
    epsilon:  0.1 
 
 
Number of Support Vectors:  970
 
But when I applied it:
 
 
> pred = predict(svm.model, data[3001:4000,1:23])
> pred[pred > .5] = 1 
> pred[pred <= .5] = 0 
> t = table(pred,data[3001:4000,24])
> t
    
pred 0   1  
   1  65 935
> classAgreement(t)
$diag
[1] 0.065
 
$kappa
[1] 0
 
$rand
[1] 0.8783283
 
$crand
[1] 0
 
It didn’t produce really good results.
 
Will best.svm get me the best svm?  Have I given it the wrong
parameters?
 
Any help most welcome.
 
Stephen

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