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Calls pendensity::pendensity from package pendensity.

Dictionary

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

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

Meta Information

  • Task type: “dens”

  • Predict Types: “pdf”, “cdf”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3extralearners, pendensity

Parameters

IdTypeDefaultLevelsRange
basecharacterbsplinebspline, gaussian\((-\infty, \infty)\)
no.basenumeric41\((-\infty, \infty)\)
max.iternumeric20\((-\infty, \infty)\)
lambda0numeric500\((-\infty, \infty)\)
qnumeric3\((-\infty, \infty)\)
sortlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
with.borderlist-\((-\infty, \infty)\)
mnumeric3\((-\infty, \infty)\)
epsnumeric0.01\((-\infty, \infty)\)

References

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensPenalized

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

LearnerDensPenalized$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerDensPenalized:dens.pen>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, pendensity
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: -
#> [1] "base"        "no.base"     "max.iter"    "lambda0"     "q"          
#> [6] "sort"        "with.border" "m"           "eps"