mlr_filters_anova {mlr3filters}

ANOVA F-Test Filter

Description

ANOVA F-Test filter calling stats::aov(). Note that this is equivalent to a t-test for binary classification.

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 -> FilterAnova

Methods

Public methods

• FilterAnova$new()

• FilterAnova$clone()

Inherited methods

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

Method new()

Create a FilterAnova object.

Usage

FilterAnova$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterAnova$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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_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, mlr_filters_variance

Examples

task = mlr3::tsk("iris")
filter = flt("anova")
filter$calculate(task)
head(as.data.table(filter), 3)

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

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

  graph$train(task)
}