mlr_fselectors_random_search {mlr3fselect}

Feature Selection with Random Search

Description

Feature selection using Random Search Algorithm.

Details

The feature sets are randomly drawn. The sets are evaluated in batches of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria.

Dictionary

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

fs("random_search")

Control Parameters

max_features

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

batch_size

integer(1)
Maximum number of feature sets to try in a batch.

Super classes

mlr3fselect::FSelector -> mlr3fselect::FSelectorBatch -> FSelectorBatchRandomSearch

Methods

Public methods

• FSelectorBatchRandomSearch$new()

• FSelectorBatchRandomSearch$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

FSelectorBatchRandomSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorBatchRandomSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281–305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

See Also

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

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("random_search"),
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10
)

# 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)