Linear Regression with Exhaustive Search

Linear Regression, where the features used in the model are selected by running Exhaustive Search.

Calls ExhaustiveSearch::ExhaustiveSearch() from ExhaustiveSearch.

Initial parameter values

• family:

  • Actual default: NULL

  • Adjusted default: "gaussian"

  • Reason for change: To comply with mlr3 architecture, we differentiate between classification and regression learners.

• nThreads:

  • Actual default: NULL

  • Adjusted default: 1

  • Reason for change: Suppressing the automatic internal parallelization if cv.folds > 0.

• quietly:

  • Actual default: FALSE

  • Adjusted default: TRUE

  • Reason for change: Suppression of constant printing to console

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.exhaustive_search")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3extralearners, ExhaustiveSearch

Parameters

Id	Type	Default	Levels	Range
family	character	-	gaussian, binomial	-
performanceMeasure	character	-	MSE, AIC	-
combsUpTo	integer	-		\([1, \infty)\)
nResults	integer	5000		\([1, \infty)\)
nThreads	integer	-		\([1, \infty)\)
testSetIDs	integer	-		\([1, \infty)\)
errorVal	untyped	-1		-
quietly	logical	-	TRUE, FALSE	-
checkLarge	logical	TRUE	TRUE, FALSE	-

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

ngerman

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrExhaustiveSearch

Methods

Public methods

• LearnerRegrExhaustiveSearch$new()

• LearnerRegrExhaustiveSearch$selected_features()

• LearnerRegrExhaustiveSearch$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()
• mlr3::LearnerRegr$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrExhaustiveSearch$new()

---

Method selected_features()

Extracts selected features of this learner.

Usage

LearnerRegrExhaustiveSearch$selected_features()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrExhaustiveSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# define learner
learner = LearnerRegrExhaustiveSearch$new()
learner$predict_type = "se"

# define task
tsk_cars = tsk("mtcars")

# train learner
learner$train(tsk_cars)

# extract selected features
learner$selected_features()
#> [1] "am"   "qsec" "wt"  

# predict on training task
learner$predict(tsk_cars)
#> 
#> ── <PredictionRegr> for 32 observations: ───────────────────────────────────────
#>  row_ids truth response        se
#>        1  21.0 22.47046 0.7196757
#>        2  21.0 22.15825 0.7435033
#>        3  22.8 26.28107 0.7597707
#>      ---   ---      ---       ---
#>       30  19.7 20.70613 0.8295549
#>       31  15.0 16.46963 1.0745511
#>       32  21.4 24.46722 0.8668433