Variance Filter

Variance filter calling stats::var().

Argument na.rm defaults to TRUE here.

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.

• Dictionary of Filters: mlr_filters

Other Filter: Filter, mlr_filters, mlr_filters_anova, mlr_filters_auc, mlr_filters_boruta, 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

Super class

mlr3filters::Filter -> FilterVariance

Methods

Public methods

• FilterVariance$new()

• FilterVariance$clone()

Inherited methods

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

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Method new()

Create a FilterVariance object.

Usage

FilterVariance$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

FilterVariance$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = mlr3::tsk("mtcars")
filter = flt("variance")
filter$calculate(task)
head(filter$scores, 3)
#>         disp           hp         qsec 
#> 15360.799829  4700.866935     3.193166 
as.data.table(filter)
#>     feature        score
#>      <char>        <num>
#>  1:    disp 1.536080e+04
#>  2:      hp 4.700867e+03
#>  3:    qsec 3.193166e+00
#>  4:     cyl 3.189516e+00
#>  5:    carb 2.608871e+00
#>  6:      wt 9.573790e-01
#>  7:    gear 5.443548e-01
#>  8:    drat 2.858814e-01
#>  9:      vs 2.540323e-01
#> 10:      am 2.489919e-01

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("variance"), filter.frac = 0.5) %>>%
    po("learner", mlr3::lrn("classif.rpart"))

  graph$train(task)
}
#> $classif.rpart.output
#> NULL
#>