Sort of. It lets you define a grid of candidate values to test and to define the rule to choose the best. For some models, it is each to come up with default values that work well (e.g. RBF SVM's, PLS, KNN) while others are more data dependent. In the latter case, the defaults may not work well.
MAx On Wed, Jan 26, 2011 at 5:45 AM, Neeti <nikkiha...@gmail.com> wrote: > > What I have understood in CARET train() method is that train() itself does > the model selection and tune the parameter. (please correct me if I am > wrong). That was my first motivation to select this package and method for > fitting the model. And use the parameter to e1071 svm() method and compare > the result. > > fit1<-train(train1,as.factor(trainset[,ncol(trainset)]),"svmpoly",trControl > = trainControl((method = "cv"),10,verboseIter = F),tuneLength=3) > > -- > View this message in context: > http://r.789695.n4.nabble.com/Train-error-subscript-out-of-bonds-tp3234510p3237800.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. > -- Max ______________________________________________ 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.
