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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, ----- TO GET MORE DETAIL...
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 -- View this message in co...