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Density logspline learner. Calls logspline::logspline() from logspline.

Dictionary

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

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

Meta Information

Parameters

IdTypeDefaultLevelsRange
lboundnumeric-\((-\infty, \infty)\)
uboundnumeric-\((-\infty, \infty)\)
maxknotsnumeric0\([0, \infty)\)
knotsuntyped--
nknotsnumeric0\([0, \infty)\)
penaltyuntyped--
silentlogicalTRUETRUE, FALSE-
mindnumeric-1\((-\infty, \infty)\)
error.actioninteger2\([0, 2]\)

References

Kooperberg, Charles, Stone, J C (1992). “Logspline density estimation for censored data.” Journal of Computational and Graphical Statistics, 1(4), 301--328.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLogspline

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

LearnerDensLogspline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# available parameters:
learner$param_set$ids()
#> [1] "lbound"       "ubound"       "maxknots"     "knots"        "nknots"      
#> [6] "penalty"      "silent"       "mind"         "error.action"