mlr3filters (version 0.7.1)

mlr_filters_mrmr: Minimum Redundancy Maximal Relevancy Filter

Description

Minimum redundancy maximal relevancy filter calling praznik::MRMR() in package praznik.

This filter supports partial scoring (see Filter).

Super class

mlr3filters::Filter -> FilterMRMR

Methods

Public methods

• FilterMRMR$new()

• FilterMRMR$clone()

Inherited methods

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

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Method new()

Create a FilterMRMR object.

Usage

FilterMRMR$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

FilterMRMR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

As the scores calculated by the praznik package are not monotone due to the greedy forward fashion, the returned scores simply reflect the selection order: 1, (k-1)/k, ..., 1/k where k is the number of selected features.

Threading is disabled by default (hyperparameter threads is set to 1). Set to a number >= 2 to enable threading, or to 0 for auto-detecting the number of available cores.

References

Kursa MB (2021). “Praznik: High performance information-based feature selection.” SoftwareX, 16, 100819. tools:::Rd_expr_doi("10.1016/j.softx.2021.100819").

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. tools:::Rd_expr_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_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_kruskal_test, mlr_filters_mim, mlr_filters_njmim, mlr_filters_performance, mlr_filters_permutation, mlr_filters_relief, mlr_filters_selected_features, mlr_filters_variance, mlr_filters

Examples

if (requireNamespace("praznik")) {
  task = mlr3::tsk("iris")
  filter = flt("mrmr")
  filter$calculate(task, nfeat = 2)
  as.data.table(filter)
}

if (mlr3misc::require_namespaces(c("mlr3pipelines", "rpart", "praznik"), quietly = TRUE)) {
  library("mlr3pipelines")
  task = mlr3::tsk("spam")

  # Note: `filter.frac` is selected randomly and should be tuned.

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

  graph$train(task)
}