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This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("dens.plug")
lrn("dens.plug")

Meta Information

  • Task type: “dens”

  • Predict Types: “pdf”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3extralearners, plugdensity

Parameters

IdTypeDefaultLevels
na.rmlogicalFALSETRUE, FALSE

References

J. Engel, Eva Herrmann and Theo Gasser (1994). An iterative bandwidth selector for kernel estimation of densities and their derivatives. Journal of Nonparametric Statistics 4, 21–34.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensPlugin

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensPlugin$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("plugdensity", quietly = TRUE)) {
  learner = mlr3::lrn("dens.plug")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerDensPlugin:dens.plug>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, plugdensity
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: missings
#> [1] "na.rm"