Filter for Embedded Feature Selection via Variable Importance

Variable Importance filter using embedded feature selection of machine learning algorithms. Takes a mlr3::Learner which is capable of extracting the variable importance (property "importance"), fits the model and extracts the importance values to use as filter scores.

See also

Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters_anova, mlr_filters_auc, mlr_filters_carscore, mlr_filters_cmim, mlr_filters_correlation, mlr_filters_disr, mlr_filters_find_correlation, mlr_filters_information_gain, mlr_filters_jmim, mlr_filters_jmi, mlr_filters_kruskal_test, mlr_filters_mim, mlr_filters_mrmr, mlr_filters_njmim, mlr_filters_performance, mlr_filters_permutation, mlr_filters_relief, mlr_filters_variance, mlr_filters

Super class

mlr3filters::Filter -> FilterImportance

Public fields

learner

(mlr3::Learner)
Learner to extract the importance values from.

Methods

Public methods

• FilterImportance$new()

• FilterImportance$clone()

Inherited methods

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

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Method new()

Create a FilterImportance object.

Usage

FilterImportance$new(learner = mlr3::lrn("classif.rpart"))

Arguments

learner

(mlr3::Learner)
Learner to extract the importance values from.

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Method clone()

The objects of this class are cloneable with this method.

Usage

FilterImportance$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = mlr3::tsk("iris")
learner = mlr3::lrn("classif.rpart")
filter = flt("importance", learner = learner)
filter$calculate(task)
as.data.table(filter)
#>         feature    score
#> 1:  Petal.Width 88.96940
#> 2: Petal.Length 81.34496
#> 3: Sepal.Length 54.09606
#> 4:  Sepal.Width 36.01309