mlr_measures_selected_features {mlr3}

Selected Features Measure

Description

Measures the number of selected features by extracting it from learners with property "selected_features". If parameter normalize is set to TRUE, the relative number of features instead of the absolute number of features is returned. Note that the models must be stored to be able to extract this information. If the learner does not support the extraction of used features, NA is returned.

This measure requires the Task and the Learner for scoring.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("selected_features")
msr("selected_features")

Meta Information

• Task type: “NA”

• Range: [0, \infty)

• Minimize: TRUE

• Average: macro

• Required Prediction: “NA”

• Required Packages: mlr3

Parameters

Id	Type	Default	Levels
normalize	logical	-	TRUE, FALSE

Super class

mlr3::Measure -> MeasureSelectedFeatures

Methods

Public methods

• MeasureSelectedFeatures$new()

• MeasureSelectedFeatures$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSelectedFeatures$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSelectedFeatures$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval

• Package mlr3measures for the scoring functions. Dictionary of Measures: mlr_measures as.data.table(mlr_measures) for a table of available Measures in the running session (depending on the loaded packages).

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

Other Measure: Measure, MeasureClassif, MeasureRegr, MeasureSimilarity, mlr_measures, mlr_measures_aic, mlr_measures_bic, mlr_measures_classif.costs, mlr_measures_debug_classif, mlr_measures_elapsed_time, mlr_measures_internal_valid_score, mlr_measures_oob_error

Examples

task = tsk("german_credit")
learner = lrn("classif.rpart")
rr = resample(task, learner, rsmp("cv", folds = 3), store_models = TRUE)

scores = rr$score(msr("selected_features"))
scores[, c("iteration", "selected_features")]