Skip to contents

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 models 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).

Super class

mlr3fselect::FSelector -> FSelectorSequential

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.`

Usage


Method optimization_path()

Returns the optimization path.

Usage

FSelectorSequential$optimization_path(inst, include_uhash = FALSE)

Arguments

inst

(FSelectInstanceSingleCrit)
Instance optimized with FSelectorSequential.

include_uhash

(logical(1))
Include uhash column?


Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorSequential$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(
  method = "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
#> 1:      FALSE        TRUE     FALSE           TRUE  FALSE FALSE FALSE
#>                      features classif.ce
#> 1: bill_length,flipper_length 0.07826087

# 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 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
#>  1: 0.26086957            0.069 2022-11-25 12:09:55        1        0      0
#>  2: 0.24347826            0.057 2022-11-25 12:09:55        1        0      0
#>  3: 0.24347826            0.059 2022-11-25 12:09:55        1        0      0
#>  4: 0.17391304            0.089 2022-11-25 12:09:55        1        0      0
#>  5: 0.27826087            0.072 2022-11-25 12:09:55        1        0      0
#>  6: 0.53043478            0.058 2022-11-25 12:09:55        1        0      0
#>  7: 0.53043478            0.058 2022-11-25 12:09:55        1        0      0
#>  8: 0.22608696            0.093 2022-11-25 12:09:56        2        0      0
#>  9: 0.07826087            0.065 2022-11-25 12:09:56        2        0      0
#> 10: 0.20000000            0.057 2022-11-25 12:09:56        2        0      0
#> 11: 0.13043478            0.060 2022-11-25 12:09:56        2        0      0
#> 12: 0.17391304            0.093 2022-11-25 12:09:56        2        0      0
#> 13: 0.17391304            0.063 2022-11-25 12:09:56        2        0      0
#>          resample_result
#>  1: <ResampleResult[21]>
#>  2: <ResampleResult[21]>
#>  3: <ResampleResult[21]>
#>  4: <ResampleResult[21]>
#>  5: <ResampleResult[21]>
#>  6: <ResampleResult[21]>
#>  7: <ResampleResult[21]>
#>  8: <ResampleResult[21]>
#>  9: <ResampleResult[21]>
#> 10: <ResampleResult[21]>
#> 11: <ResampleResult[21]>
#> 12: <ResampleResult[21]>
#> 13: <ResampleResult[21]>

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