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Feature Selection with Sequential Search

Source: R/FSelectorBatchSequential.R
mlr_fselectors_sequential.Rd

Feature selection using Sequential Search Algorithm.

Details

Sequential forward selection (strategy = fsf) extends the feature set in each iteration with the feature that increases the model's performance the most. Sequential backward selection (strategy = fsb) follows the same idea but starts with all features and removes features from the set.

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

Dictionary

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

fs("sequential")

Control Parameters

min_features

integer(1)
Minimum number of features. By default, 1.

max_features

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

strategy

character(1)
Search method sfs (forward search) or sbs (backward search).

See also

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

Super classes

mlr3fselect::FSelector -> mlr3fselect::FSelectorBatch -> FSelectorBatchSequential

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.`

Usage

FSelectorBatchSequential$new()

Method optimization_path()

Returns the optimization path.

Usage

FSelectorBatchSequential$optimization_path(inst, include_uhash = FALSE)

Arguments

inst

(FSelectInstanceBatchSingleCrit)
Instance optimized with FSelectorBatchSequential.

include_uhash

(logical(1))
Include uhash column?

Returns

data.table::data.table()

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorBatchSequential$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("sequential"),
  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:      FALSE        TRUE     FALSE           TRUE  FALSE  FALSE  FALSE
#>                      features n_features classif.ce
#>                        <list>      <int>      <num>
#> 1: bill_length,flipper_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       FALSE     FALSE           TRUE  FALSE  FALSE  FALSE
#>  9:      FALSE        TRUE     FALSE           TRUE  FALSE  FALSE  FALSE
#> 10:      FALSE       FALSE      TRUE           TRUE  FALSE  FALSE  FALSE
#> 11:      FALSE       FALSE     FALSE           TRUE   TRUE  FALSE  FALSE
#> 12:      FALSE       FALSE     FALSE           TRUE  FALSE   TRUE  FALSE
#> 13:      FALSE       FALSE     FALSE           TRUE  FALSE  FALSE   TRUE
#>     classif.ce runtime_learners           timestamp batch_nr warnings errors
#>          <num>            <num>              <POSc>    <int>    <int>  <int>
#>  1: 0.24347826            0.005 2025-01-16 10:17:57        1        0      0
#>  2: 0.22608696            0.005 2025-01-16 10:17:57        1        0      0
#>  3: 0.28695652            0.005 2025-01-16 10:17:57        1        0      0
#>  4: 0.22608696            0.005 2025-01-16 10:17:57        1        0      0
#>  5: 0.26086957            0.005 2025-01-16 10:17:57        1        0      0
#>  6: 0.59130435            0.005 2025-01-16 10:17:57        1        0      0
#>  7: 0.59130435            0.005 2025-01-16 10:17:57        1        0      0
#>  8: 0.22608696            0.006 2025-01-16 10:17:57        2        0      0
#>  9: 0.06956522            0.005 2025-01-16 10:17:57        2        0      0
#> 10: 0.22608696            0.006 2025-01-16 10:17:57        2        0      0
#> 11: 0.19130435            0.005 2025-01-16 10:17:57        2        0      0
#> 12: 0.22608696            0.005 2025-01-16 10:17:57        2        0      0
#> 13: 0.22608696            0.005 2025-01-16 10:17:57        2        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,flipper_length          2 <ResampleResult>
#>  9: bill_length,flipper_length          2 <ResampleResult>
#> 10:   body_mass,flipper_length          2 <ResampleResult>
#> 11:      flipper_length,island          2 <ResampleResult>
#> 12:         flipper_length,sex          2 <ResampleResult>
#> 13:        flipper_length,year          2 <ResampleResult>

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