FSelectorExhaustiveSearch: Feature Selection via Exhaustive Search

Description Dictionary Parameters Super class Methods Examples

Description

FSelectorExhaustiveSearch class that implements an Exhaustive Search.

In order to support general termination criteria and parallelization, feature sets are evaluated in batches. The size of the feature sets is increased by 1 in each batch.

Dictionary

This FSelector can be instantiated via the dictionary mlr_fselectors or with the associated sugar function fs():

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mlr_fselectors$get("exhaustive_search")
fs("exhaustive_search")

Parameters

max_features

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

Super class

mlr3fselect::FSelector -> FSelectorExhaustiveSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
FSelectorExhaustiveSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
FSelectorExhaustiveSearch$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

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library(mlr3)

terminator = trm("evals", n_evals = 5)

instance = FSelectInstanceSingleCrit$new(
  task = tsk("iris"),
  learner = lrn("classif.rpart"),
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  terminator = terminator
)

fselector = fs("exhaustive_search")

# Modifies the instance by reference
fselector$optimize(instance)

# Returns best scoring evaluation
instance$result

# Allows access of data.table of full path of all evaluations
as.data.table(instance$archive)

mlr3fselect documentation built on March 9, 2021, 5:06 p.m.