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Density estimation using penalized B-splines with automatic selection of smoothing parameter. Calls pendensity::pendensity() from 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

Parameters

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

References

Schellhase, Christian, Kauermann, Göran (2012). “Density estimation and comparison with a penalized mixture approach.” Computational Statistics, 27(4), 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

learner = mlr3::lrn("dens.pen")
print(learner)
#> <LearnerDensPenalized:dens.pen>: Penalized Density Estimation
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, pendensity
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: -

# available parameters:
learner$param_set$ids()
#> [1] "base"        "no.base"     "max.iter"    "lambda0"     "q"          
#> [6] "sort"        "with.border" "m"           "eps"