mlr3fselect (version 0.2.1)

FSelectorRandomSearch: Feature Selection via Random Search

Description

FSelectorRandomSearch class that implements a simple Random Search.

In order to support general termination criteria and parallelization, we evaluate feature sets in a batch-fashion 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 via the dictionary mlr_fselectors or with the associated sugar function fs():

mlr_fselectors$get("random_search")
fs("random_search")

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 class

mlr3fselect::FSelector -> FSelectorRandomSearch

Methods

Public methods

• FSelectorRandomSearch$new()

• FSelectorRandomSearch$clone()

• mlr3fselect::FSelector$format()

• mlr3fselect::FSelector$optimize()

• mlr3fselect::FSelector$print()

Method new()

Creates a new instance of this R6 class.

Usage

FSelectorRandomSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorRandomSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# NOT RUN {
library(mlr3)

terminator = trm("evals", n_evals = 10)

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

fselector = fs("random_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
instance$archive$data
# }