R help - Mar 2012 - e1071 SVM: Cross-validation error confusion matrix

Hi, 

I ran two svm models in R e1071 package: the first without cross-validation
and the second with 10-fold cross-validation.

I used the following syntax: 

#Model 1: Without cross-validation: 
> svm.model <- svm(Response ~ ., data=data.df,
type="C-classification",
> kernel="linear", cost=1) 
> predict <- fitted(svm.model)
> cm <- table(predict, data.df$Response)
> cm
#Model2: With 10-fold cross-validation: 
> svm.model2 <- svm(Response ~ ., data=data.df,
type="C-classification",
> kernel="linear", cost=1, cross=10) 
> predict2 <- fitted(svm.model2)
> cm2 <- table(predict2, data.df$Response)
> cm2
However, when I compare cm and cm2, I notice that the confusion matrices are
identical although the accuracy of each model is diffent. What am I doing
wrong?
 
Thanks for you help,

Chris

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Did you ever receive a response to this? I did not see one public.

I would think that if your dataset was of a large enough size, that 10-fold
validation would show an improvement over N:N.

Also, any ideas if there is any difference really in using fitted() vs.
predict() in your second step? I am pretty sure they do the same thing.  

Brian



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