Calls gss::ssden from package gss.

Dictionary

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

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

Traits

  • Packages: gss

  • Predict Types: pdf, cdf

  • Feature Types: integer, numeric

  • Properties: missings

References

Gu, C. and Wang, J. (2003), Penalized likelihood density estimation: Direct cross-validation and scalable approximation. Statistica Sinica, 13, 811–826.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensSpline

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensSpline$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensSpline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("dens.spline")) print(learner)
#> <LearnerDensSpline:dens.spline> #> * Model: - #> * Parameters: list() #> * Packages: gss #> * Predict Type: pdf #> * Feature types: integer, numeric #> * Properties: missings
# available parameters: learner$param_set$ids()
#> [1] "type" "alpha" "weights" "na.action" "id.basis" #> [6] "nbasis" "seed" "domain" "quad" "qdsz.depth" #> [11] "bias" "prec" "maxiter" "skip.iter"