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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