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,