Hi all, I am struggling to understand kernel based method. I am trying to understand two SVM method (CARET::train and e1071::svm()) I will try to put my question in following points: 1. About e1071::svm(), on what basis the final model is selected when we use cross =10 parameter in svm()? Is it based on accuracy? How to retrieve the final model? I understand that CARET::train method select the final model based on best accuracy but fail to understand e1071::svm() method. 2. I understand that e1071::SVM() scale the data with Mean0Stdev1 Scaling method (please correct me if I am wrong). Which is the method used in CARET::train scale the data? 3. Following is the code I am using to select model to compair train() and svm(); CARET::train fit1<-train(train1,as.factor(trainset[,ncol(trainset)]),"svmpoly",trControl = trainControl((method = "cv"),10,verboseIter = F),tuneLength=3) tune_best<-fit1$bestTune; degree1<-tune_best[[2]] c1<-tune_best[[1]] e1071::svm() model_true1 <- svm(train1,species,kernel="polynomial",degree = degree1,cost = c1,scale=T) degree and c1 is the values that has been taken form train method() and used in svm();(sorry if this part is not clear) Now the question is, am I correct to say that both should give me approximately similar result (or model)? As this code suggests that I am using similar parameters and in principle I am using same kernel method. Should I compare both the result? -- View this message in context: http://r.789695.n4.nabble.com/Comparison-between-Train-and-SVM-method-tp3275780p3275780.html Sent from the R help mailing list archive at Nabble.com.