Feature Selection with Exhaustive Search

Feature Selection using the Exhaustive Search Algorithm. Exhaustive Search generates all possible feature sets.

Details

The feature selection terminates itself when all feature sets are evaluated. It is not necessary to set a termination criterion.

Dictionary

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

fs("exhaustive_search")

Control Parameters

max_features

integer(1)
Maximum number of features. By default, number of features in mlr3::Task.

See also

Other FSelector: mlr_fselectors, mlr_fselectors_design_points, mlr_fselectors_genetic_search, mlr_fselectors_random_search, mlr_fselectors_rfe, mlr_fselectors_rfecv, mlr_fselectors_sequential, mlr_fselectors_shadow_variable_search

Super class

mlr3fselect::FSelector -> FSelectorExhaustiveSearch

Methods

Public methods

• FSelectorExhaustiveSearch$new()

• FSelectorExhaustiveSearch$clone()

Inherited methods

• mlr3fselect::FSelector$format()
• mlr3fselect::FSelector$help()
• mlr3fselect::FSelector$optimize()
• mlr3fselect::FSelector$print()

---

Method new()

Creates a new instance of this R6 class.

Usage

FSelectorExhaustiveSearch$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorExhaustiveSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# 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("exhaustive_search"),
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10
)

# best performing feature set
instance$result
#>    bill_depth bill_length body_mass flipper_length island    sex   year
#>        <lgcl>      <lgcl>    <lgcl>         <lgcl> <lgcl> <lgcl> <lgcl>
#> 1:       TRUE        TRUE     FALSE          FALSE  FALSE  FALSE  FALSE
#>                  features n_features classif.ce
#>                    <list>      <int>      <num>
#> 1: bill_depth,bill_length          2 0.06956522

# all evaluated feature sets
as.data.table(instance$archive)
#>     bill_depth bill_length body_mass flipper_length island    sex   year
#>         <lgcl>      <lgcl>    <lgcl>         <lgcl> <lgcl> <lgcl> <lgcl>
#>  1:       TRUE       FALSE     FALSE          FALSE  FALSE  FALSE  FALSE
#>  2:      FALSE        TRUE     FALSE          FALSE  FALSE  FALSE  FALSE
#>  3:      FALSE       FALSE      TRUE          FALSE  FALSE  FALSE  FALSE
#>  4:      FALSE       FALSE     FALSE           TRUE  FALSE  FALSE  FALSE
#>  5:      FALSE       FALSE     FALSE          FALSE   TRUE  FALSE  FALSE
#>  6:      FALSE       FALSE     FALSE          FALSE  FALSE   TRUE  FALSE
#>  7:      FALSE       FALSE     FALSE          FALSE  FALSE  FALSE   TRUE
#>  8:       TRUE        TRUE     FALSE          FALSE  FALSE  FALSE  FALSE
#>  9:       TRUE       FALSE      TRUE          FALSE  FALSE  FALSE  FALSE
#> 10:       TRUE       FALSE     FALSE           TRUE  FALSE  FALSE  FALSE
#>     classif.ce runtime_learners           timestamp batch_nr warnings errors
#>          <num>            <num>              <POSc>    <int>    <int>  <int>
#>  1: 0.20869565            0.007 2024-03-09 11:48:40        1        0      0
#>  2: 0.23478261            0.005 2024-03-09 11:48:40        1        0      0
#>  3: 0.26956522            0.007 2024-03-09 11:48:40        1        0      0
#>  4: 0.20869565            0.004 2024-03-09 11:48:40        1        0      0
#>  5: 0.34782609            0.005 2024-03-09 11:48:40        1        0      0
#>  6: 0.61739130            0.004 2024-03-09 11:48:40        1        0      0
#>  7: 0.62608696            0.004 2024-03-09 11:48:40        1        0      0
#>  8: 0.06956522            0.004 2024-03-09 11:48:40        1        0      0
#>  9: 0.19130435            0.004 2024-03-09 11:48:40        1        0      0
#> 10: 0.16521739            0.005 2024-03-09 11:48:40        1        0      0
#>                      features n_features  resample_result
#>                        <list>     <list>           <list>
#>  1:                bill_depth          1 <ResampleResult>
#>  2:               bill_length          1 <ResampleResult>
#>  3:                 body_mass          1 <ResampleResult>
#>  4:            flipper_length          1 <ResampleResult>
#>  5:                    island          1 <ResampleResult>
#>  6:                       sex          1 <ResampleResult>
#>  7:                      year          1 <ResampleResult>
#>  8:    bill_depth,bill_length          2 <ResampleResult>
#>  9:      bill_depth,body_mass          2 <ResampleResult>
#> 10: bill_depth,flipper_length          2 <ResampleResult>

# subset the task and fit the final model
task$select(instance$result_feature_set)
learner$train(task)
# }