mlr3proba (version 0.4.9)

mlr_learners_dens.kde: Kernel Density Estimator

Description

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

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, distr

• Feature Types: integer, numeric

• Properties: missings

• Packages: mlr3 mlr3proba distr6

Super classes

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

Methods

Public methods

• LearnerDensKDE$new()

• LearnerDensKDE$clone()

• mlr3::Learner$base_learner()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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.

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. The default kernel is Epanechnikov (chosen to reduce dependencies).

References

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

See Also

Other density estimators: mlr_learners_dens.hist