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")

Traits

• Packages: sm

• Predict Types: pdf

• Feature Types: integer, numeric

• Properties: weights

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

Author

RaphaelS1

Super classes

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

Methods

Public methods

• LearnerDensNonparametric$new()

• LearnerDensNonparametric$clone()

Inherited methods

• 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()

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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

# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("dens.nonpar"))
print(learner)
#> <LearnerDensNonparametric:dens.nonpar>
#> * Model: -
#> * Parameters: list()
#> * Packages: 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"