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Density Smoothing Splines Learner. Calls gss::ssden() from 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")

Meta Information

Parameters

IdTypeDefaultLevelsRange
typeuntyped--
alphanumeric1.4\((-\infty, \infty)\)
weightsuntyped--
na.actionuntyped:: , stats , na.omit-
id.basisuntyped--
nbasisinteger-\((-\infty, \infty)\)
seednumeric-\((-\infty, \infty)\)
domainuntyped--
quaduntyped--
qdsz.depthnumeric-\((-\infty, \infty)\)
biasuntyped--
precnumeric1e-07\((-\infty, \infty)\)
maxiterinteger30\([1, \infty)\)
skip.iterlogical-TRUE, FALSE-

References

Gu, Chong, Wang, Jingyuan (2003). “Penalized likelihood density estimation: Direct cross-validation and scalable approximation.” Statistica Sinica, 811--826.

See also

Author

RaphaelS1

Super classes

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

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

LearnerDensSpline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("dens.spline")
print(learner)
#> <LearnerDensSpline:dens.spline>: Density Smoothing Splines
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, 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"