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Kernel density estimator. Calls ks::kde() from ks.

Dictionary

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

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

Meta Information

Parameters

IdTypeDefaultLevelsRange
hnumeric-\([0, \infty)\)
Huntyped--
gridsizeuntyped--
gridtypeuntyped--
xminnumeric-\((-\infty, \infty)\)
xmaxnumeric-\((-\infty, \infty)\)
suppnumeric3.7\((-\infty, \infty)\)
binnednumeric-\((-\infty, \infty)\)
bgridsizeuntyped--
positivelogicalFALSETRUE, FALSE-
adj.positiveuntyped--
wuntyped--
compute.contlogicalTRUETRUE, FALSE-
approx.contlogicalTRUETRUE, FALSE-
unit.intervallogicalFALSETRUE, FALSE-
verboselogicalFALSETRUE, FALSE-
densitylogicalFALSETRUE, FALSE-

References

Gramacki, Artur, Gramacki, Jarosław (2017). “FFT-based fast computation of multivariate kernel density estimators with unconstrained bandwidth matrices.” Journal of Computational and Graphical Statistics, 26(2), 459--462.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensKDEks

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

LearnerDensKDEks$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("dens.kde_ks")
print(learner)
#> <LearnerDensKDEks:dens.kde_ks>: Kernel Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, ks
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: -

# available parameters:
learner$param_set$ids()
#>  [1] "h"             "H"             "gridsize"      "gridtype"     
#>  [5] "xmin"          "xmax"          "supp"          "binned"       
#>  [9] "bgridsize"     "positive"      "adj.positive"  "w"            
#> [13] "compute.cont"  "approx.cont"   "unit.interval" "verbose"      
#> [17] "density"