caret's recursive feature selection with randomForest. While running
rfe function with method repeatedcv, I had parameter maximize = TRUE. Thus, optimal set of variables is decided based on the best RMSE metrics.
However, I would like to see the minimum "tolerable" set of predictor variables without rerunning rfe with parameter maximize = FALSE. It takes 24 hours to rerun my analysis.
Appearently, caret's function
pickSizeTolerance does the trick, as is described on caret's webpage: http://caret.r-forge.r-project.org/featureselection.html
How to use the existing rfe object to get the "tolerable" set of variables?
library(caret) inTrain <- createDataPartition(y = iris[,4], p = .66, list = FALSE) training <- iris[ inTrain,] testing <- iris[-inTrain,] ctrl <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 5, verbose = TRUE, returnResamp = "all") rfProfile <- rfe(training[,-4], training[,4], sizes = c(2,3), rfeControl = ctrl, newdata = testing[,-4])
rfProfile$resample includes all the metrics, but how to calculate?