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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

Inherited methods


Method new()

Create a FilterInformationGain object.

Usage


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.

Examples

if (requireNamespace("FSelectorRcpp")) {
  ## InfoGain (default)
  task = mlr3::tsk("pima")
  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)

}
#>    feature      score
#> 1: glucose 0.09829336
#> 2:    mass 0.08428940
#> 3:     age 0.07257535

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)

}
#> $classif.rpart.output
#> NULL
#>