> On Oct 5, 2014, at 4:51 PM, Lorenzo Isella <lorenzo.ise...@gmail.com> wrote: > > Thanks a lot. > At this point then I wonder: seen that my response consists of 5 > outcomes for each set of features, should I then train 5 different > models (one for each of them)? > Cheers
caret can only model one outcome at a time so yes. Max > Lorenzo > >> On Sun, Oct 05, 2014 at 11:04:01AM -0700, Jia Xu wrote: >> Hi, Lorenzo: >> For 1) I think the formula is not correct. The formula should be outcome >> ~ features, and that's why you have weird result in 3) >> 2) predict in caret will automatically find the best result one if >> there is one(sometimes it fails). You can print the model to see the cross >> validation result. Furthermore, you may specify the performance metric you >> want to find the optimal result. Please see the details of the caret >> tutorial to see how to. >> >> On Sun, Oct 5, 2014 at 8:54 AM, Lorenzo Isella <lorenzo.ise...@gmail.com> >> wrote: >> >>> Dear All, >>> I am learning the ropes of CARET for automatic model training, more or >>> less following the steps of the tutorial at >>> >>> http://bit.ly/ZJQINa >>> >>> However, there are a few things about which I would like a piece of >>> advice. >>> >>> Consider for instance the following model >>> >>> ############################################################# >>> >>> set.seed(825) >>> >>> fitControl <- trainControl(## 10-fold CV >>> method = "repeatedcv", >>> number = 10, >>> ## repeated ten times >>> repeats = 10) >>> >>> gbmGrid <- expand.grid(interaction.depth = c(1, 5, 9), >>> n.trees = (1:30)*50, >>> shrinkage = 0.05) >>> >>> nrow(gbmGrid) >>> >>> gbmFit <- train(Ca+P+pH+SOC+Sand~ ., data = training, >>> method = "gbm", >>> trControl = fitControl, >>> ## This last option is actually one >>> ## for gbm() that passes through >>> verbose = TRUE, >>> ## Now specify the exact models ## to evaludate: >>> tuneGrid = gbmGrid >>> ) >>> >>> ############################################################# >>> >>> I am trying to tune a model that predicts the values of 5 columns >>> whose names are "Ca","P","pH", "SOC", and "Sand". >>> >>> 1) Am I using the formula syntax in a correct way? >>> >>> I then try to apply my model on the test data by coding >>> >>> mypred <- predict(gbmFit, newdata=test) >>> >>> However, at this point I am left with a couple of questions >>> >>> 2) does "predict" automatically select the best tuned model in gbmFit? >>> and if not, what am I supposed to do? >>> 3) I do not get any error messages, but mypred consists of a single >>> column instead of 5 columns corresponding to the 5 variables I am >>> trying to predict, so something is obviously wrong (see point 1). Any >>> suggestions here? >>> >>> Many thanks >>> >>> Lorenzo >>> >>> ______________________________________________ >>> 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. >> >> >> >> -- >> Jia Xu > > ______________________________________________ > 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. ______________________________________________ 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.
