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Calls sm::sm.density from package sm.

Dictionary

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

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

Meta Information

  • Task type: “dens”

  • Predict Types: “pdf”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3extralearners, sm

Parameters

IdTypeDefaultLevelsRange
hnumeric-\((-\infty, \infty)\)
grouplist-\((-\infty, \infty)\)
deltanumeric-\((-\infty, \infty)\)
h.weightsnumeric1\((-\infty, \infty)\)
hmultlist1\((-\infty, \infty)\)
methodcharacternormalnormal, cv, sj, df, aicc\((-\infty, \infty)\)
positivelogicalFALSETRUE, FALSE\((-\infty, \infty)\)
verboselist1\((-\infty, \infty)\)

References

Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensNonparametric

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensNonparametric$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerDensNonparametric:dens.nonpar>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, sm
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: weights
#> [1] "h"         "group"     "delta"     "h.weights" "hmult"     "method"   
#> [7] "positive"  "verbose"