r-caret

I'm trying to build a predictive model in caret using PCA as pre-processing. The pre-processing would be as follows: preProc <- preProcess(IL_train[,-1], method="pca", thresh = 0.8) Is it possible to pass the thresh argument directly to caret's train() function? I've tried the following, but it doesn't work: modelFit_pp <- train(IL_train$diagnosis ~ . , preProcess="pca", thresh= 0.8, method="glm", data=IL_train) If not, how can I pass the separate preProc results to the train() function? As per the documentation, you specify additional preprocessing arguments with trainControl ?trainControl ..

I have a problem with specifying the learning rate using the caret package with the method "mlpWeightDecay" from RSNNS package. The tuning parameters of "mlpWeightDecay" are size and decay. An example leaving size constant at 4 and tuning decay over c(0,0.0001, 0.001, 0.002): data(iris) TrainData <- iris[,1:4] TrainClasses <- iris[,5] fit1 <- train(TrainData, TrainClasses, method = "mlpWeightDecay", preProcess = c("center", "scale"), tuneGrid=expand.grid(.size = 4, .decay = c(0,0.0001, 0.001, 0.002)), trControl = trainControl(method = "cv") ) But I also want to manipulate the learning rate of

I noticed that using formula and non-formula methods in caret while training produces different results. Also, the time taken for formula method is almost 10x the time taken for the non-formula method. Is this expected ? > z <- data.table(c1=sample(1:1000,1000, replace=T), c2=as.factor(sample(LETTERS, 1000, replace=T))) # SYSTEM TIME WITH FORMULA METHOD # ------------------------------- > system.time(r <- train(c1 ~ ., z, method="rf", importance=T)) user system elapsed 376.233 9.241 18.190 > r 1000 samples 1 predictors No pre-processing Resampling: Bootstrap (25 reps) Summary of sample sizes:

I'm using the caret package to analyse Random Forest models built using ranger . I can't figure out how to call the train function using the tuneGrid argument to tune the model parameters. I think I'm calling the tuneGrid argument wrong, but can't figure out why it's wrong. Any help would be appreciated. data(iris) library(ranger) model_ranger <- ranger(Species ~ ., data = iris, num.trees = 500, mtry = 4, importance = 'impurity') library(caret) # my tuneGrid object: tgrid <- expand.grid( num.trees = c(200, 500, 1000), mtry = 2:4 ) model_caret <- train(Species ~ ., data = iris, method = "ranger

I've been dealing with some extremely imbalanced data and I would like to use stratified sampling to created more balanced random forests Right now, I'm using the caret package, mainly to for tuning the random forests. So I try to setup a tuneGrid to pass in the mtry and sampsize parameters into caret train method as follows. mtryGrid <- data.frame(.mtry = 100),.sampsize=80) rfTune<- train(x = trainX, y = trainY, method = "rf", trControl = ctrl, metric = "Kappa", ntree = 1000, tuneGrid = mtryGrid, importance = TRUE) When I run this example, I get the following error The tuning parameter grid

In R, I specify a model with no intercept as follows: data(iris) lmFit <- lm(Sepal.Length ~ 0 + Petal.Length + Petal.Width, data=iris) > round(coef(lmFit),2) Petal.Length Petal.Width 2.86 -4.48 However, if I fit the same model with caret, the resulting model includes an intercept: library(caret) caret_lmFit <- train(Sepal.Length~0+Petal.Length+Petal.Width, data=iris, "lm") > round(coef(caret_lmFit$finalModel),2) (Intercept) Petal.Length Petal.Width 4.19 0.54 -0.32 How do I tell caret::train to exclude the intercept term? As discussed in a linked SO question https://stackoverflow.com/a/41731117

I have been trying to apply recursive feature selection using caret package. What I need is that ref uses the AUC as performance measure. After googling for a month I cannot get the process working. Here is the code I have used: library(caret) library(doMC) registerDoMC(cores = 4) data(mdrr) subsets <- c(1:10) ctrl <- rfeControl(functions=caretFuncs, method = "cv", repeats =5, number = 10, returnResamp="final", verbose = TRUE) trainctrl <- trainControl(classProbs= TRUE) caretFuncs$summary <- twoClassSummary set.seed(326) rf.profileROC.Radial <- rfe(mdrrDescr, mdrrClass, sizes=subsets,