search for: trainclass

...train function from the caret package to train a svm with a spectral kernel from the kernlab package. Sadly a svm with spectral kernel is not among the many methods in caret... using caret to train svmRadial: ------------------ library(caret) library(kernlab) data(iris) TrainData<- iris[,1:4] TrainClasses<- iris[,5] set.seed(2) fitControl$summaryFunction<- Rand svmNew<- train(TrainData, TrainClasses, method = "svmRadial", preProcess = c("center", "scale"), metric = "cRand", tuneLength = 4) svmNew ------------------- here is an example on...

Dear all, I am using the "caret" Package for predictors selection with a randomForest model. The following is the train function: rfFit<- train(x=trainRatios, y=trainClass, method="rf", importance = TRUE, do.trace = 100, keep.inbag = TRUE, tuneGrid = grid, trControl=bootControl, scale = TRUE, metric = "ROC") I wanted to use ROC as the metric for variable selection. I know that this works with the logit model by making sure that classProbs =...

...to follow the instructions in the manual and the vignettes but unfortunately I´m getting an error message I can`t figure out. Here is my code: rfControl <- trainControl(method = "oob", returnResamp = "all", returnData=TRUE, verboseIter = TRUE) rftrain <- train(x=train_x, y=trainclass, method="rf", tuneGrid=tuneGrid, tr.control=rfControl) pred <- predict(rftrain) pred # this works fine expred <- extractPrediction(rftrain) Error in models[[1]]$trainingData : $ operator is invalid for atomic vectors My predictors are 28 numeric attributes and one factor. I`m...

I am used to packages like e1071 where you have a tune step and then pass your tunings to train. It seems with caret, tuning and training are both handled by train. I am using train and trainControl to find my hyper parameters like so: MyTrainControl=trainControl( method = "cv", number=5, returnResamp = "all", classProbs = TRUE ) rbfSVM <- train(label~., data =