Recursive feature selection with cross-validation in the caret package (R) The rfe functions in the caret package allow to perform recursive feature selection (backward) with cross-validation.
It is expected that the best features selected in each fold may differ, as also stated in the caret webpage

Another complication to using resampling is that multiple lists of the
  “best” predictors are generated at each iteration. At first this may
  seem like a disadvantage, but it does provide a more probabilistic
  assessment of predictor importance than a ranking based on a single
  fixed data set. At the end of the algorithm, a consensus ranking can
  be used to determine the best predictors to retain.

However it is not clear to me how the final "best" set of predictors is chosen in rfe, considering this expected heterogeneity among folds. I cannot find the procedure of the "consensus ranking" mentioned above.
Thank you for you help!
 A: I am sorry i am adding an answer here as I cannot comment yet to Brian's answer (consider this a comment to that one), but the answers to this query below also deals with this.
Find variables selected for each subset using caret feature selection
In short, rfe seems to compute 1 'overall' important ranking for each predictor once using all predictors on entire dataset. (So the "Overall" column for a predictor in rfe's $variable output remains same for different folds.)
You can see this score in the column "Overall" by calling $Variablesto your rfe, like below (using Max's code from link above):
data(BloodBrain)

x <- scale(bbbDescr[,-nearZeroVar(bbbDescr)])
x <- x[, -findCorrelation(cor(x), .8)]
x <- as.data.frame(x)

set.seed(1)
lmProfile <- rfe(x, logBBB,
             sizes = 10:20,
             rfeControl = rfeControl(functions = lmFuncs, 
                                     number = 15))
lmProfile$variables

Looking at the output, we see that when rfe selects variables for a subset size, different variables may get selected at different resampling folds.
To find out the best predictors in the required subset size (say 10-fold), rfe sums up the "Overall" importance ranking for a predictor every time it appears in a fold in the 10-fold subset. This is how they rank the predictors. [I have written a crude code to replicate this in the query link above.]
The final best predictors of the subset size are the predictors with highest summed ranks. 
(At least this is how I make sense of it; I hope I am right!)
