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Kernel Density Estimator of the distribution function. Calls kerdiest::kde() from kerdiest.

Dictionary

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

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

Meta Information

Parameters

IdTypeDefaultLevelsRange
bwnumeric-\([0, \infty)\)
type_kernelcharacternn, e, t, b-

References

Quintela-del-Río, Alejandro, Estévez-Pérez G (2012). “Nonparametric kernel distribution function estimation with kerdiest: an R package for bandwidth choice and applications.” Journal of Statistical Software, 50, 1--21.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensKDEkd

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

LearnerDensKDEkd$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("dens.kde_kd")
#> Warning: Package 'kerdiest' required but not installed for Learner 'dens.kde_kd'
print(learner)
#> <LearnerDensKDEkd:dens.kde_kd>: Kernel Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, kerdiest
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: -

# available parameters:
learner$param_set$ids()
#> [1] "bw"          "type_kernel"