A simple backwards selection, a.k.a. recursive feature elimination (RFE), algorithm
rfe(x, ...) # S3 method for default rfe(x, y, sizes = 2^(2:4), metric = ifelse(is.factor(y), "Accuracy", "RMSE"), maximize = ifelse(metric == "RMSE", FALSE, TRUE), rfeControl = rfeControl(), ...) # S3 method for formula rfe(form, data, ..., subset, na.action, contrasts = NULL) rfeIter(x, y, testX, testY, sizes, rfeControl = rfeControl(), label = "", seeds = NA, ...) # S3 method for rfe update(object, x, y, size, ...) # S3 method for recipe rfe(x, data, sizes = 2^(2:4), metric = NULL, maximize = NULL, rfeControl = rfeControl(), ...)
| x | A matrix or data frame of predictors for model training. This
object must have unique column names. For the recipes method, |
|---|---|
| ... | options to pass to the model fitting function (ignored in
|
| y | a vector of training set outcomes (either numeric or factor) |
| sizes | a numeric vector of integers corresponding to the number of features that should be retained |
| metric | a string that specifies what summary metric will be used to
select the optimal model. By default, possible values are "RMSE" and
"Rsquared" for regression and "Accuracy" and "Kappa" for classification. If
custom performance metrics are used (via the |
| maximize | a logical: should the metric be maximized or minimized? |
| rfeControl | a list of options, including functions for fitting and prediction. The web page http://topepo.github.io/caret/recursive-feature-elimination.html#rfe has more details and examples related to this function. |
| form | A formula of the form |
| data | Data frame from which variables specified in
|
| subset | An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) |
| na.action | A function to specify the action to be taken
if NAs are found. The default action is for the procedure to
fail. An alternative is |
| contrasts | A list of contrasts to be used for some or all the factors appearing as variables in the model formula. |
| testX | a matrix or data frame of test set predictors. This must have
the same column names as |
| testY | a vector of test set outcomes |
| label | an optional character string to be printed when in verbose mode. |
| seeds | an optional vector of integers for the size. The vector should
have length of |
| object | an object of class |
| size | a single integers corresponding to the number of features that should be retained in the updated model |
A list with elements
a list of size
length(sizes) + 1 containing the column names of the ``surviving''
predictors at each stage of selection. The first element corresponds to all
the predictors (i.e. size = ncol(x))
a data frame with columns for the test set outcome, the predicted outcome and the subset size.
More details on this function can be found at http://topepo.github.io/caret/recursive-feature-elimination.html.
This function implements backwards selection of predictors based on predictor importance ranking. The predictors are ranked and the less important ones are sequentially eliminated prior to modeling. The goal is to find a subset of predictors that can be used to produce an accurate model. The web page http://topepo.github.io/caret/recursive-feature-elimination.html#rfe has more details and examples related to this function.
rfe can be used with "explicit parallelism", where different
resamples (e.g. cross-validation group) can be split up and run on multiple
machines or processors. By default, rfe will use a single processor
on the host machine. As of version 4.99 of this package, the framework used
for parallel processing uses the foreach package. To run the resamples
in parallel, the code for rfe does not change; prior to the call to
rfe, a parallel backend is registered with foreach (see the
examples below).
rfeIter is the basic algorithm while rfe wraps these
operations inside of resampling. To avoid selection bias, it is better to
use the function rfe than rfeIter.
When updating a model, if the entire set of resamples were not saved using
rfeControl(returnResamp = "final"), the existing resamples are
removed with a warning.
We using a recipe as an input, there may be some subset sizes that are not well-replicated over resamples. `rfe` method will only consider subset sizes where at least half of the resamples have associated results in the search for an optimal subset size.
if (FALSE) { 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 = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, number = 200)) set.seed(1) lmProfile2 <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, rerank = TRUE, number = 200)) xyplot(lmProfile$results$RMSE + lmProfile2$results$RMSE ~ lmProfile$results$Variables, type = c("g", "p", "l"), auto.key = TRUE) rfProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = rfFuncs)) bagProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = treebagFuncs)) set.seed(1) svmProfile <- rfe(x, logBBB, sizes = c(2, 5, 10, 20), rfeControl = rfeControl(functions = caretFuncs, number = 200), ## pass options to train() method = "svmRadial") ## classification data(mdrr) mdrrDescr <- mdrrDescr[,-nearZeroVar(mdrrDescr)] mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .8)] set.seed(1) inTrain <- createDataPartition(mdrrClass, p = .75, list = FALSE)[,1] train <- mdrrDescr[ inTrain, ] test <- mdrrDescr[-inTrain, ] trainClass <- mdrrClass[ inTrain] testClass <- mdrrClass[-inTrain] set.seed(2) ldaProfile <- rfe(train, trainClass, sizes = c(1:10, 15, 30), rfeControl = rfeControl(functions = ldaFuncs, method = "cv")) plot(ldaProfile, type = c("o", "g")) postResample(predict(ldaProfile, test), testClass) } ####################################### ## Parallel Processing Example via multicore if (FALSE) { library(doMC) ## Note: if the underlying model also uses foreach, the ## number of cores specified above will double (along with ## the memory requirements) registerDoMC(cores = 2) set.seed(1) lmProfile <- rfe(x, logBBB, sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65), rfeControl = rfeControl(functions = lmFuncs, number = 200)) }