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...ies I had to add " classProbs = TRUE," in the trainControl. Thereafter everytime I run train I get this message: "undefined columns selected" I copy the syntax: fitControl <- trainControl(method = "cv", number = 10, classProbs = TRUE,returnResamp = "all", verboseIter = FALSE) glmFit <- train(Descr, Categ, method = "glmStepAIC",tuneLength = 4,trControl = fitControl) Thank you. Best regards, Jon Toledo, MD Postdoctoral fellow University of Pennsylvania School of Medicine Center for Neurodegenerative Disease Research 3600 Spruce Street 3rd Floor Mal...

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 =

...n1<-train1[,-(1)] test_t<-testset[,-ncol(testset)] species_test<-as.factor(testset[,ncol(testset)]) test_t<-test_t[,-(1)] #### #CARET::TRAIN #### fit1<-train(train1,as.factor(trainset[,ncol(trainset)]),"svmpoly",trControl = trainControl((method = "cv"),10,verboseIter = F),tuneLength=3) pred<-predict(fit1,test_t) t_train[[i]]<-table(predicted=pred,observed=testset[,ncol(testset)]) tune_result[[i]]<-fit1$results; tune_best<-fit1$bestTune; scale1[i]<-tune_best[[3]] degree[i]<-tune_best[[2]] c1[i]<-tune_best[[1]] } -- View this mes...

...odel and make predictions on a test dataset. I tried 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 pred...

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +