mlr3fselect (version 1.3.0)

mlr_fselectors_sequential: Feature Selection with Sequential Search

Description

Feature selection using Sequential Search Algorithm.

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 classes

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

Methods

Public methods

• FSelectorBatchSequential$new()

• FSelectorBatchSequential$optimization_path()

• FSelectorBatchSequential$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

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.

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.

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

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

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

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