mlr3 (version 0.5.0)

mlr_measures_selected_features: Selected Features Measure

Description

Measures the number of selected features by extracting it from learners with property "selected_features". If the learner does not support this, 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

• Type: NA

• Range: \([0, \infty)\)

• Minimize: TRUE

• Required prediction: 'response'

Super class

mlr3::Measure -> MeasureSelectedFeatures

Public fields

normalize

(logical(1)) If set to TRUE, divides the number of features by the total number of features.

Methods

Public methods

• MeasureSelectedFeatures$new()

• MeasureSelectedFeatures$clone()

• 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(normalize = FALSE)

Arguments

normalize

(logical(1)) If set to TRUE, divides the number of features by the total number of features.

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

Dictionary of Measures: mlr_measures

as.data.table(mlr_measures) for a complete table of all (also dynamically created) Measure implementations.

Other Measure: MeasureClassif, MeasureRegr, Measure, mlr_measures_classif.costs, mlr_measures_debug, mlr_measures_elapsed_time, mlr_measures_oob_error, mlr_measures