mlr3filters (version 0.3.0)

mlr_filters_importance: 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.

Super class

mlr3filters::Filter -> FilterImportance

Public fields

learner

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

Methods

Public methods

• FilterImportance$new()

• FilterImportance$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• mlr3filters::Filter$print()

Method new()

Create a FilterImportance object.

Usage

FilterImportance$new(
  id = "importance",
  task_type = learner$task_type,
  feature_types = learner$feature_types,
  learner = mlr3::lrn("classif.rpart"),
  packages = learner$packages,
  param_set = learner$param_set
)

Arguments

id

(character(1)) Identifier for the filter.

task_type

(character()) Types of the task the filter can operator on. E.g., "classif" or "regr".

feature_types

(character()) Feature types the filter operates on. Must be a subset of mlr_reflections$task_feature_types.

learner

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

packages

(character()) Set of required packages. Note that these packages will be loaded via requireNamespace(), and are not attached.

param_set

(paradox::ParamSet) Set of hyperparameters.

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.

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_variance, 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)
# }