caret (version 6.0-73)

rfe: Backwards Feature Selection

Description

A simple backwards selection, a.k.a. recursive feature selection (RFE), algorithm

Usage

rfe(x, ...)
"rfe"(x, y, sizes = 2^(2:4), metric = ifelse(is.factor(y), "Accuracy", "RMSE"), maximize = ifelse(metric == "RMSE", FALSE, TRUE), rfeControl = rfeControl(), ...)
rfeIter(x, y, testX, testY, sizes, rfeControl = rfeControl(), label = "", seeds = NA, ...)
"update"(object, x, y, size, ...)

Arguments

x

a matrix or data frame of predictors for model training. This object must have unique column names.

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 functions argument in rfeControl, the value of metric should match one of the arguments.

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/featureselection.html#rfe has more details and examples related to this function.

testX

a matrix or data frame of test set predictors. This must have the same column names as x

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 length(sizes)+1

object

an object of class rfe

size

a single integers corresponding to the number of features that should be retained in the updated model

...

options to pass to the model fitting function (ignored in predict.rfe)

Value

A list with elements A list with elements

Details

More details on this function can be found at http://topepo.github.io/caret/featureselection.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/featureselection.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.

See Also

rfeControl

Examples

## Not run: 
# 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)
# 
# ## End(Not run)

#######################################
## Parallel Processing Example via multicore

## Not run: 
# 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))
# 
# 
# ## End(Not run)