mlr3filters (version 0.1.0)

FilterImportance: Filter for Embedded Feature Selection via Variable Importance

Description

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.

Format

R6::R6Class inheriting from Filter.

Construction

FilterImportance$new(learner = mlr3::lrn("classif.rpart"))
mlr_filters$get("importance")
flt("importance")

• learner :: mlr3::Learner Learner to extract the importance values from.

See Also

Dictionary of Filters: mlr_filters

Other Filter: FilterAUC, FilterAnova, FilterCMIM, FilterCarScore, FilterCorrelation, FilterDISR, FilterInformationGain, FilterJMIM, FilterJMI, FilterKruskalTest, FilterMIM, FilterMRMR, FilterNJMIM, FilterPerformance, FilterVariance, Filter, mlr_filters

Examples

# NOT RUN {
task = mlr3::tsk("iris")
learner = mlr3::lrn("classif.rpart")
filter = flt("importance", learner = learner)
filter$calculate(task)
as.data.table(filter)
# }