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Burota Filter

Source: R/FilterBoruta.R
mlr_filters_boruta.Rd

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.

Initial parameter values

  • num.threads:

    • Actual default: NULL, triggering auto-detection of the number of CPUs.

    • Adjusted value: 1.

    • Reason for change: Conflicting with parallelization via future.

References

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

See also

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

Super class

mlr3filters::Filter -> FilterBoruta

Methods

Public methods

Inherited methods

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.

Examples

# \donttest{
  if (requireNamespace("Boruta")) {
   task = mlr3::tsk("sonar")
   filter = flt("boruta")
   filter$calculate(task)
   as.data.table(filter)
  }
#> Loading required namespace: Boruta
#>     feature score
#>      <char> <num>
#>  1:     V16     1
#>  2:     V15     1
#>  3:     V27     1
#>  4:      V4     1
#>  5:     V44     1
#>  6:      V9     1
#>  7:     V52     1
#>  8:     V17     1
#>  9:     V31     1
#> 10:     V26     1
#> 11:     V13     1
#> 12:     V49     1
#> 13:     V46     1
#> 14:     V19     1
#> 15:     V18     1
#> 16:     V10     1
#> 17:     V23     1
#> 18:     V45     1
#> 19:     V36     1
#> 20:      V1     1
#> 21:     V47     1
#> 22:     V22     1
#> 23:      V5     1
#> 24:     V51     1
#> 25:     V20     1
#> 26:     V37     1
#> 27:     V48     1
#> 28:     V35     1
#> 29:     V21     1
#> 30:     V28     1
#> 31:     V12     1
#> 32:     V11     1
#> 33:      V7     0
#> 34:     V29     0
#> 35:     V55     0
#> 36:     V42     0
#> 37:     V40     0
#> 38:     V34     0
#> 39:     V32     0
#> 40:     V60     0
#> 41:     V25     0
#> 42:     V39     0
#> 43:      V3     0
#> 44:     V56     0
#> 45:     V24     0
#> 46:     V54     0
#> 47:     V14     0
#> 48:     V53     0
#> 49:     V30     0
#> 50:      V2     0
#> 51:     V41     0
#> 52:     V57     0
#> 53:      V6     0
#> 54:      V8     0
#> 55:     V43     0
#> 56:     V33     0
#> 57:     V58     0
#> 58:     V38     0
#> 59:     V50     0
#> 60:     V59     0
#>     feature score
# }