mlr_filters_boruta {mlr3filters}

Burota Filter

Description

Filter using the Boruta algorithm for feature selection. If keep = "tentative", confirmed and tentative features are returned. Note that there is no ordering in the selected features. Selected features get a score of 1, deselected features get a score of 0. The order of selected features is random. In combination with mlr3pipelines, only the filter criterion cutoff makes sense.

Super class

mlr3filters::Filter -> FilterBoruta

Methods

Public methods

• FilterBoruta$new()

• FilterBoruta$clone()

Inherited methods

• mlr3filters::Filter$calculate()
• mlr3filters::Filter$format()
• mlr3filters::Filter$help()
• mlr3filters::Filter$print()

Method new()

Creates a new instance of this R6 class.

Usage

FilterBoruta$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterBoruta$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Kursa MB, Rudnicki WR (2010). “Feature Selection with the Boruta Package.” Journal of Statistical Software, 36(11), 1-13.

See Also

• PipeOpFilter for filter-based feature selection.

• Dictionary of Filters: mlr_filters

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

Examples

  if (requireNamespace("Boruta")) {
   task = mlr3::tsk("sonar")
   filter = flt("boruta")
   filter$calculate(task)
   as.data.table(filter)
  }