r-caret

I am new to caret, and I just want to ensure that I fully understand what it’s doing. Towards that end, I’ve been attempting to replicate the results I get from a randomForest() model using caret’s train() function for method="rf". Unfortunately, I haven’t been able to get matching results, and I’m wondering what I’m overlooking. I’ll also add that given that randomForest uses bootstrapping to generate samples to fit each of the ntrees, and estimates error based on out-of-bag predictions, I’m a little fuzzy on the difference between specifying "oob" and "boot" in the trainControl function call

Is there a way to perform stratified cross validation when using the train function to fit a model to a large imbalanced data set? I know straight forward k fold cross validation is possible but my categories are highly unbalanced. I've seen discussion about this topic but no real definitive answer. Thanks in advance. There is a parameter called 'index' which can let user specified the index to do cross validation. folds <- 4 cvIndex <- createFolds(factor(training$Y), folds, returnTrain = T) tc <- trainControl(index = cvIndex, method = 'cv', number = folds) rfFit <- train(Y ~ ., data =

I do not understand which is the difference between varImp function ( caret package) and importance function ( randomForest package) for a Random Forest model: I computed a simple RF classification model and when computing variable importance, I found that the "ranking" of predictors was not the same for both functions: Here is my code: rfImp <- randomForest(Origin ~ ., data = TAll_CS, ntree = 2000, importance = TRUE) importance(rfImp) BREAST LUNG MeanDecreaseAccuracy MeanDecreaseGini Energy_GLCM_R1SC4NG3 -1.44116806 2.8918537 1.0929302 0.3712622 Contrast_GLCM_R1SC4NG3 -2.61146974 1.5848150 -0

I am trying to use the hmeasure metric Hand,2009 as my custom metric for training SVMs in caret. As I am relatively new to using R, I tried adapt the twoClassSummary function. All I need is to pass the true class labels and predicted class probability from the model (an svm) to the HMeasure function from the hmeasure package instead of using ROC or other measures of classification performance in caret. For example, a call to the HMeasure function in R - HMeasure(true.class,predictedProbs[,2])- results in calculation of the Hmeasure. Using an adaptation of twoClassSummary code below results in

I am trying to estimate a logistic regression, using the 10-fold cross-validation. #import libraries library(car); library(caret); library(e1071); library(verification) #data import and preparation data(Chile) chile <- na.omit(Chile) #remove "na's" chile <- chile[chile$vote == "Y" | chile$vote == "N" , ] #only "Y" and "N" required chile$vote <- factor(chile$vote) #required to remove unwanted levels chile$income <- factor(chile$income) # treat income as a factor Goal is to estimate a glm - model that predicts to outcome of vote "Y" or "N" depended on relevant explanatory variables and, based on

I would like to export a Caret random forest model using the pmml library so I can use it for predictions in Java. Here is a reproduction of the error I am getting. data(iris) require(caret) require(pmml) rfGrid2 <- expand.grid(.mtry = c(1,2)) fitControl2 <- trainControl( method = "repeatedcv", number = NUMBER_OF_CV, repeats = REPEATES) model.Test <- train(Species ~ ., data = iris, method ="rf", trControl = fitControl2, ntree = NUMBER_OF_TREES, importance = TRUE, tuneGrid = rfGrid2) print(model.Test) pmml(model.Test) Error in UseMethod("pmml") : no applicable method for 'pmml' applied to an