Kernel Density Estimator

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

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

References

Silverman, W. B (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

• LearnerDensKDE$new()

• LearnerDensKDE$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensKDE$new()

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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.