Calls kernels implemented in distr6 and the result is coerced to a distr6::Distribution.

Details

The default bandwidth uses Silverman's rule-of-thumb for Gaussian kernels, however for non-Gaussian kernels it is recommended to use mlr3tuning to tune the bandwidth with cross-validation. Other density learners can be used for automated bandwidth selection.

Dictionary

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

LearnerDensKDE$new()
mlr_learners$get("dens.kde")
lrn("dens.kde")

Meta Information

  • Type: "dens"

  • Predict Types: pdf

  • Feature Types: logical, integer, numeric, character, factor, ordered

  • Properties: missings

  • Packages: distr6

References

Silverman BW (1986). Density Estimation for Statistics and Data Analysis. Chapman \& Hall, London.

See also

Other density estimators: mlr_learners_dens.hist

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensKDE

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensKDE$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensKDE$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.