Density Nonparametric Learner

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

Id	Type	Default	Levels	Range
h	numeric	-		\((-\infty, \infty)\)
group	list	-		\((-\infty, \infty)\)
delta	numeric	-		\((-\infty, \infty)\)
h.weights	numeric	1		\((-\infty, \infty)\)
hmult	list	1		\((-\infty, \infty)\)
method	character	normal	normal, cv, sj, df, aicc	\((-\infty, \infty)\)
positive	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
verbose	list	1		\((-\infty, \infty)\)

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

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

RaphaelS1

Super classes

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

Methods

Public methods

• LearnerDensNonparametric$new()

• LearnerDensNonparametric$clone()

Inherited methods

• mlr3::Learner$base_learner()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensNonparametric$new()

---

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"