是否可以使用mlr进行递归特征消除（rfe）？我知道在caret中可以做到这一点，这里有介绍，但即使mlr中有关于特征选择的文档，我也没有找到与rfe相当的方法。
回答：
要在mlr中进行递归特征消除，您可以使用函数makeFeatSelControlSequential，并设置参数method = sbs（顺序向后选择）。以下是使用lda学习器的使用示例：
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这里，60个变量中有57个被选中。
您可以使用：
analyzeFeatSelResult(sfeats)来查看选择路径
#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.