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2012 Dec 02
1
e1071 SVM: Cross-validation error confusion matrix
...r", 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,
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2012 Mar 02
1
e1071 SVM: Cross-validation error confusion matrix
...near", 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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