I have a model like the following:

```
library(mlbench)
data(Sonar)

library(caret)
set.seed(998)

my_data <- Sonar

fitControl <-
  trainControl(
    method = "cv",
    number = 10,
    classProbs = T,
    savePredictions = T,
    summaryFunction = twoClassSummary
  )

model <- train(
  Class ~ .,
  data = my_data,
  method = "xgbTree",
  trControl = fitControl,
  metric = "ROC"
)

```

However, using 10 fold cross validation, it keeps the folds that are chosen constant based on the rows in which the samples appear in the training data. 

How can I make it randomly select 10% of the training data as a resample for cross validation? I believe this is referred to as Monte Carlo cross validation.

Thanks!