Logistic Regression with Exhaustive Search

Logistic 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: "binomial"

  • 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("classif.exhaustive_search")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• 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	-1L		-
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::LearnerClassif -> LearnerClassifExhaustiveSearch

Methods

Public methods

• LearnerClassifExhaustiveSearch$new()

• LearnerClassifExhaustiveSearch$selected_features()

• LearnerClassifExhaustiveSearch$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::LearnerClassif$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifExhaustiveSearch$new()

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Method selected_features()

Extracts selected features of this learner.

Usage

LearnerClassifExhaustiveSearch$selected_features()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifExhaustiveSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# define the learner
learner = lrn("classif.exhaustive_search", predict_type = "prob", combsUpTo = 3)

# define the task
# and subset to 3 features to speed up the example
tsk_sonar = tsk("sonar")$select(c("V1", "V2", "V3"))

# train the learner
learner$train(tsk_sonar)

# extract selected features
learner$selected_features()
#> [1] "V1"

# predict on training task
learner$predict(tsk_sonar)
#> 
#> ── <PredictionClassif> for 208 observations: ───────────────────────────────────
#>  row_ids truth response    prob.M    prob.R
#>        1     R        R 0.4753214 0.5246786
#>        2     R        M 0.6664160 0.3335840
#>        3     R        M 0.5237325 0.4762675
#>      ---   ---      ---       ---       ---
#>      206     M        M 0.7125284 0.2874716
#>      207     M        M 0.5555589 0.4444411
#>      208     M        M 0.5221729 0.4778271