mlr_fselectors_shadow_variable_search: Feature Selection with Shadow Variable Search

In mlr3fselect: Feature Selection for 'mlr3'

Feature Selection with Shadow Variable Search

Description

Feature selection using the Shadow Variable Search Algorithm. Shadow variable search creates for each feature a permutated copy and stops when one of them is selected.

Details

The feature selection terminates itself when the first shadow variable is selected. It is not necessary to set a termination criterion.

Resources

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

• Run a feature selection with Shadow Variable Search.

Dictionary

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

fs("shadow_variable_search")

Super classes

mlr3fselect::FSelector -> mlr3fselect::FSelectorBatch -> FSelectorBatchShadowVariableSearch

Methods

Public methods

• FSelectorBatchShadowVariableSearch$new()

• FSelectorBatchShadowVariableSearch$optimization_path()

• FSelectorBatchShadowVariableSearch$clone()

Inherited methods

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

---

Method new()

Creates a new instance of this R6 class.'

Usage

FSelectorBatchShadowVariableSearch$new()

---

Method optimization_path()

Returns the optimization path.

Usage

FSelectorBatchShadowVariableSearch$optimization_path(inst)

Arguments

inst

(FSelectInstanceBatchSingleCrit)
Instance optimized with FSelectorBatchShadowVariableSearch.

Returns

data.table::data.table

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorBatchShadowVariableSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Source

Thomas J, Hepp T, Mayr A, Bischl B (2017). “Probing for Sparse and Fast Variable Selection with Model-Based Boosting.” Computational and Mathematical Methods in Medicine, 2017, 1–8. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1155/2017/1421409")}.

Wu Y, Boos DD, Stefanski LA (2007). “Controlling Variable Selection by the Addition of Pseudovariables.” Journal of the American Statistical Association, 102(477), 235–243. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/016214506000000843")}.

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_sequential

Examples

# Feature Selection


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

# best performing feature subset
instance$result

# all evaluated feature subsets
as.data.table(instance$archive)

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

mlr3fselect documentation built on Sept. 11, 2024, 7:36 p.m.