It is possible to conduct a recursive feature elimination feature (rfe) with mlr ? I know this is possible with caret here but even if there is some documentation about feature selection with mlr, I did not find an equivalent to rfe.
To perform recursive feature elimination in mlr you can use the function makeFeatSelControlSequential with the argument method = sbs (sequential backwards selection). Here is an example of usage using lda learner :
library(mlr)
ctrl <- makeFeatSelControlSequential(method = "sbs",
beta = 0.005)
rdesc <- makeResampleDesc("CV", iters = 3)
sfeats <- selectFeatures(learner = "classif.lda",
task = sonar.task,
resampling = rdesc,
control = ctrl,
show.info = FALSE)
FeatSel result:
Features (57): V1, V2, V3, V4, V5, V6, V7, V8, V9, V11, V12, V13, V14, V15, V16, V17, V18, V19, V21, V22, V23, V24, V25, V26, V27, V28, V29, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39, V40, V41, V42, V43, V44, V45, V46, V47, V48, V49, V50, V51, V52, V53, V54, V55, V56, V57, V58, V60
mmce.test.mean=0.2066943
here, 57 variables out of 60 were selected.
you can use:
analyzeFeatSelResult(sfeats)
to get a hold of the selection path
#output
Path to optimum:
- Features: 60 Init : Perf = 0.26936 Diff: NA *
- Features: 59 Remove : V59 Perf = 0.2403 Diff: 0.029055 *
- Features: 58 Remove : V10 Perf = 0.22588 Diff: 0.014424 *
- Features: 57 Remove : V20 Perf = 0.20669 Diff: 0.019186 *
Stopped, because no improving feature was found.