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Feature Selection with Genetic Search

Source: R/FSelectorGeneticSearch.R
mlr_fselectors_genetic_search.Rd

Feature selection using the Genetic Algorithm from the package genalg.

Dictionary

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

fs("genetic_search")

Control Parameters

For the meaning of the control parameters, see genalg::rbga.bin(). genalg::rbga.bin() internally terminates after iters iteration. We set ìters = 100000 to allow the termination via our terminators. If more iterations are needed, set ìters to a higher value in the parameter set.

See also

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

Super class

mlr3fselect::FSelector -> FSelectorGeneticSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

FSelectorGeneticSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorGeneticSearch$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("genetic_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
#> 1:      FALSE       FALSE     FALSE           TRUE  FALSE FALSE FALSE
#>          features classif.ce
#> 1: flipper_length  0.2173913

# all evaluated feature sets
as.data.table(instance$archive)
#>     bill_depth bill_length body_mass flipper_length island   sex  year
#>  1:       TRUE       FALSE     FALSE          FALSE  FALSE FALSE  TRUE
#>  2:      FALSE       FALSE     FALSE           TRUE  FALSE FALSE FALSE
#>  3:      FALSE       FALSE     FALSE           TRUE  FALSE FALSE FALSE
#>  4:      FALSE       FALSE     FALSE          FALSE   TRUE FALSE FALSE
#>  5:      FALSE        TRUE     FALSE          FALSE  FALSE FALSE FALSE
#>  6:       TRUE       FALSE      TRUE          FALSE  FALSE FALSE  TRUE
#>  7:      FALSE       FALSE      TRUE          FALSE  FALSE FALSE FALSE
#>  8:       TRUE       FALSE     FALSE          FALSE  FALSE FALSE FALSE
#>  9:      FALSE       FALSE     FALSE           TRUE  FALSE FALSE FALSE
#> 10:      FALSE       FALSE     FALSE           TRUE  FALSE FALSE FALSE
#>     classif.ce runtime_learners           timestamp batch_nr warnings errors
#>  1:  0.2869565            0.005 2023-11-17 12:02:36        1        0      0
#>  2:  0.2173913            0.005 2023-11-17 12:02:36        2        0      0
#>  3:  0.2173913            0.004 2023-11-17 12:02:36        3        0      0
#>  4:  0.2956522            0.006 2023-11-17 12:02:36        4        0      0
#>  5:  0.2695652            0.005 2023-11-17 12:02:36        5        0      0
#>  6:  0.2608696            0.005 2023-11-17 12:02:36        6        0      0
#>  7:  0.2869565            0.005 2023-11-17 12:02:36        7        0      0
#>  8:  0.2869565            0.004 2023-11-17 12:02:36        8        0      0
#>  9:  0.2173913            0.005 2023-11-17 12:02:36        9        0      0
#> 10:  0.2173913            0.004 2023-11-17 12:02:36       10        0      0
#>                      features      resample_result
#>  1:           bill_depth,year <ResampleResult[21]>
#>  2:            flipper_length <ResampleResult[21]>
#>  3:            flipper_length <ResampleResult[21]>
#>  4:                    island <ResampleResult[21]>
#>  5:               bill_length <ResampleResult[21]>
#>  6: bill_depth,body_mass,year <ResampleResult[21]>
#>  7:                 body_mass <ResampleResult[21]>
#>  8:                bill_depth <ResampleResult[21]>
#>  9:            flipper_length <ResampleResult[21]>
#> 10:            flipper_length <ResampleResult[21]>

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