mlr_filters_information_gain: Information Gain Filter

In mlr3filters: Filter Based Feature Selection for 'mlr3'

Information Gain Filter

Description

Information gain filter calling FSelectorRcpp::information_gain() in package FSelectorRcpp. Set parameter "type" to "gainratio" to calculate the gain ratio, or set to "symuncert" to calculate the symmetrical uncertainty (see FSelectorRcpp::information_gain()). Default is "infogain".

Argument equal defaults to FALSE for classification tasks, and to TRUE for regression tasks.

Super class

mlr3filters::Filter -> FilterInformationGain

Methods

Public methods

• FilterInformationGain$new()

• FilterInformationGain$clone()

Inherited methods

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

---

Method new()

Create a FilterInformationGain object.

Usage

FilterInformationGain$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FilterInformationGain$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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_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

if (requireNamespace("FSelectorRcpp")) {
  ## InfoGain (default)
  task = mlr3::tsk("sonar")
  filter = flt("information_gain")
  filter$calculate(task)
  head(filter$scores, 3)
  as.data.table(filter)

  ## GainRatio

  filterGR = flt("information_gain")
  filterGR$param_set$values = list("type" = "gainratio")
  filterGR$calculate(task)
  head(as.data.table(filterGR), 3)

}

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

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

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

  graph$train(task)

}

mlr3filters documentation built on Sept. 13, 2025, 1:08 a.m.