mlr3fselect (version 0.2.1)

FSelectorSequential: Feature Selection via Sequential Selection

Description

FSelectorSequential class that implements sequential feature selection. The sequential forward selection (strategy = fsf) extends the feature set in each step with the feature that increases the models performance the most. The sequential backward selection (strategy = fsb) starts with the complete future set and removes in each step the feature that decreases the models performance the least.

Dictionary

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

mlr_fselectors$get("sequential")
fs("sequential")

Parameters

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 class

mlr3fselect::FSelector -> FSelectorSequential

Methods

Public methods

• FSelectorSequential$new()

• FSelectorSequential$optimization_path()

• FSelectorSequential$clone()

• mlr3fselect::FSelector$format()

• mlr3fselect::FSelector$optimize()

• mlr3fselect::FSelector$print()

Method new()

Creates a new instance of this R6 class.`

Usage

FSelectorSequential$new()

Method optimization_path()

Returns the optimization path.

Usage

FSelectorSequential$optimization_path(inst)

Arguments

inst

(FSelectInstanceSingleCrit) Instance optimized with FSelectorSequential.

Returns

data.table::data.table

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorSequential$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("sequential")

# 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
# }