Kruskal-Wallis Test Filter

Kruskal-Wallis rank sum test filter calling stats::kruskal.test().

The filter value is -log10(p) where p is the \(p\)-value. This transformation is necessary to ensure numerical stability for very small \(p\)-values.

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_mim, mlr_filters_mrmr, mlr_filters_njmim, mlr_filters_performance, mlr_filters_variable_importance, mlr_filters_variance, mlr_filters

Super class

mlr3filters::Filter -> FilterKruskalTest

Methods

Public methods

• FilterKruskalTest$new()

• FilterKruskalTest$clone()

Inherited methods

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$print()

---

Method new()

Create a FilterKruskalTest object.

Usage

FilterKruskalTest$new(
  id = "kruskal_test",
  task_type = "classif",
  param_set = ParamSet$new(list(ParamFct$new("na.action", default = "na.omit", levels =
    c("na.omit", "na.fail", "na.exclude", "na.pass")))),
  packages = "stats",
  feature_types = c("integer", "numeric")
)

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".

param_set

(paradox::ParamSet)
Set of hyperparameters.

packages

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

feature_types

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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterKruskalTest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = mlr3::tsk("iris")
filter = flt("kruskal_test")
filter$calculate(task)
as.data.table(filter)
#>         feature    score
#> 1:  Petal.Width 28.48654
#> 2: Petal.Length 28.31840
#> 3: Sepal.Length 21.04970
#> 4:  Sepal.Width 13.80430

# transform to p-value
10^(-filter$scores)
#>  Petal.Width Petal.Length Sepal.Length  Sepal.Width 
#> 3.261796e-29 4.803974e-29 8.918734e-22 1.569282e-14