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.
References
For a benchmark of filter methods:
Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020). “Benchmark for filter methods for feature selection in high-dimensional classification data.” Computational Statistics & Data Analysis, 143, 106839. doi:10.1016/j.csda.2019.106839 .
See also
PipeOpFilter for filter-based feature selection.
Other Filter:
Filter
,
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_jmim
,
mlr_filters_jmi
,
mlr_filters_mim
,
mlr_filters_mrmr
,
mlr_filters_njmim
,
mlr_filters_performance
,
mlr_filters_permutation
,
mlr_filters_relief
,
mlr_filters_selected_features
,
mlr_filters_variance
,
mlr_filters
Super class
mlr3filters::Filter
-> FilterKruskalTest
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
if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart"), quietly = TRUE)) {
library("mlr3pipelines")
task = mlr3::tsk("spam")
# Note: `filter.frac` is selected randomly and should be tuned.
graph = po("filter", filter = flt("kruskal_test"), filter.frac = 0.5) %>>%
po("learner", mlr3::lrn("classif.rpart"))
graph$train(task)
}
#> $classif.rpart.output
#> NULL
#>