FSelectorRandomSearch: Feature Selection via Random Search

Description Dictionary Parameters Super class Methods Source Examples

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():

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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

Inherited methods

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.

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.

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("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
as.data.table(instance$archive)

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