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Nonparametric density estimation. Calls sm::sm.density() from 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

Parameters

IdTypeDefaultLevelsRange
hnumeric-\((-\infty, \infty)\)
groupuntyped--
deltanumeric-\((-\infty, \infty)\)
h.weightsnumeric1\((-\infty, \infty)\)
hmultuntyped1-
methodcharacternormalnormal, cv, sj, df, aicc-
positivelogicalFALSETRUE, FALSE-
verboseuntyped1-

References

Bowman, A.W., Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations, series Oxford Statistical Science Series. OUP Oxford. ISBN 9780191545696, https://books.google.de/books?id=7WBMrZ9umRYC.

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

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

# available parameters:
learner$param_set$ids()
#> [1] "h"         "group"     "delta"     "h.weights" "hmult"     "method"   
#> [7] "positive"  "verbose"