mlr3filters (version 0.3.0)

mlr_filters_kruskal_test: Kruskal-Wallis Test Filter

Description

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.

Super class

mlr3filters::Filter -> FilterKruskalTest

Methods

Public methods

• FilterKruskalTest$new()

• FilterKruskalTest$clone()

• mlr3filters::Filter$calculate()

• mlr3filters::Filter$format()

• mlr3filters::Filter$help()

• 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.

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_importance, mlr_filters_information_gain, mlr_filters_jmim, mlr_filters_jmi, 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")
filter = flt("kruskal_test")
filter$calculate(task)
as.data.table(filter)

# transform to p-value
10^(-filter$scores)
# }