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Feature selection using the Recursive Feature Elimination (RFE) algorithm. Recursive feature elimination iteratively removes features with a low importance score. Only works with mlr3::Learners that can calculate importance scores (see the section on optional extractors in mlr3::Learner).


Guyon I, Weston J, Barnhill S, Vapnik V (2002). “Gene Selection for Cancer Classification using Support Vector Machines.” Machine Learning, 46(1), 389--422. ISSN 1573-0565, doi:10.1023/A:1012487302797 .


The learner is trained on all features at the start and importance scores are calculated for each feature. Then the least important feature is removed and the learner is trained on the reduced feature set. The importance scores are calculated again and the procedure is repeated until the desired number of features is reached. The non-recursive option (recursive = FALSE) only uses the importance scores calculated in the first iteration.

The feature selection terminates itself when n_features is reached. It is not necessary to set a termination criterion.

When using a cross-validation resampling strategy, the importance scores of the resampling iterations are aggregated. The parameter aggregation determines how the importance scores are aggregated. By default ("rank"), the importance score vector of each fold is ranked and the feature with the lowest average rank is removed. The option "mean" averages the score of each feature across the resampling iterations and removes the feature with the lowest average score. Averaging the scores is not appropriate for most importance measures.


The ArchiveBatchFSelect holds the following additional columns:

  • "importance" (numeric())
    The importance score vector of the feature subset.


The gallery features a collection of case studies and demos about optimization.


This FSelector can be instantiated with the associated sugar function fs():


Control Parameters


The minimum number of features to select, by default half of the features.


Fraction of features to retain in each iteration. The default of 0.5 retains half of the features.


Number of features to remove in each iteration.


Vector of the number of features to retain in each iteration. Must be sorted in decreasing order.


If TRUE (default), the feature importance is calculated in each iteration.


The aggregation method for the importance scores of the resampling iterations. See details.

The parameter feature_fraction, feature_number and subset_sizes are mutually exclusive.

Super classes

mlr3fselect::FSelector -> mlr3fselect::FSelectorBatch -> FSelectorBatchRFE


Inherited methods

Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.


FSelectorBatchRFE$clone(deep = FALSE)



Whether to make a deep clone.


# Feature Selection
# \donttest{

# retrieve task and load learner
task = tsk("penguins")
learner = lrn("classif.rpart")

# run feature selection on the Palmer Penguins data set
instance = fselect(
  fselector = fs("rfe"),
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  store_models = TRUE

# best performing feature subset
#>    bill_depth bill_length body_mass flipper_length island    sex   year
#>        <lgcl>      <lgcl>    <lgcl>         <lgcl> <lgcl> <lgcl> <lgcl>
#> 1:       TRUE        TRUE     FALSE           TRUE  FALSE  FALSE  FALSE
#>    importance                              features n_features classif.ce
#>        <list>                                <list>      <int>      <num>
#> 1:      3,2,1 bill_depth,bill_length,flipper_length          3 0.08695652

# all evaluated feature subsets$archive)
#>    bill_depth bill_length body_mass flipper_length island    sex   year
#>        <lgcl>      <lgcl>    <lgcl>         <lgcl> <lgcl> <lgcl> <lgcl>
#> 1:       TRUE        TRUE      TRUE           TRUE   TRUE   TRUE   TRUE
#> 2:       TRUE        TRUE     FALSE           TRUE  FALSE  FALSE  FALSE
#>    classif.ce runtime_learners           timestamp batch_nr warnings errors
#>         <num>            <num>              <POSc>    <int>    <int>  <int>
#> 1: 0.08695652            0.011 2024-05-13 08:21:52        1        0      0
#> 2: 0.08695652            0.009 2024-05-13 08:21:52        2        0      0
#>         importance
#>             <list>
#> 1: 7,6,5,4,3,2,...
#> 2:           3,2,1
#>                                                          features n_features
#>                                                            <list>     <list>
#> 1: bill_depth,bill_length,body_mass,flipper_length,island,sex,...          7
#> 2:                          bill_depth,bill_length,flipper_length          3
#>     resample_result
#>              <list>
#> 1: <ResampleResult>
#> 2: <ResampleResult>

# subset the task and fit the final model
# }