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, ----- TO GET MORE DETAILS CLICK HERE -- View this message in context: http://r.789695.n4.nabble.com/e1071-SVM-Cross-validation-error-confusion-matrix-tp4651652.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
