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Density estimator for discrete and continuous variables. Calls np::npudens() from np.

Dictionary

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

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

Meta Information

Parameters

IdTypeDefaultLevelsRange
bwsuntyped--
ckertypecharactergaussiangaussian, epanechnikov, uniform-
bwscalinglogicalFALSETRUE, FALSE-
bwmethodcharactercv.mlcv.ml, cv.ls, normal-reference-
bwtypecharacterfixedfixed, generalized_nn, adaptive_nn-
bandwidth.computelogicalFALSETRUE, FALSE-
ckerorderinteger2\([2, 8]\)
reminlogicalTRUETRUE, FALSE-
itmaxinteger10000\([1, \infty)\)
nmultiinteger-\([1, \infty)\)
ftolnumeric1.490116e-07\((-\infty, \infty)\)
tolnumeric0.0001490116\((-\infty, \infty)\)
smallnumeric1.490116e-05\((-\infty, \infty)\)
lbc.dirnumeric0.5\((-\infty, \infty)\)
dfc.dirnumeric0.5\((-\infty, \infty)\)
cfac.diruntyped* , 2.5 , (3 - sqrt(5))-
initc.dirnumeric1\((-\infty, \infty)\)
lbd.dirnumeric0.1\((-\infty, \infty)\)
hbd.dirnumeric1\((-\infty, \infty)\)
dfac.diruntyped* , 0.25 , (3 - sqrt(5))-
initd.dirnumeric1\((-\infty, \infty)\)
lbc.initnumeric0.1\((-\infty, \infty)\)
hbc.initnumeric2\((-\infty, \infty)\)
cfac.initnumeric0.5\((-\infty, \infty)\)
lbd.initnumeric0.1\((-\infty, \infty)\)
hbd.initnumeric0.9\((-\infty, \infty)\)
dfac.initnumeric0.37\((-\infty, \infty)\)
ukertypecharacter-aitchisonaitken, liracine-
okertypecharacter-wangvanryzin, liracine-

References

Li, Qi, Racine, Jeff (2003). “Nonparametric estimation of distributions with categorical and continuous data.” journal of multivariate analysis, 86(2), 266--292.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensMixed

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

LearnerDensMixed$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# available parameters:
learner$param_set$ids()
#>  [1] "bws"               "ckertype"          "bwscaling"        
#>  [4] "bwmethod"          "bwtype"            "bandwidth.compute"
#>  [7] "ckerorder"         "remin"             "itmax"            
#> [10] "nmulti"            "ftol"              "tol"              
#> [13] "small"             "lbc.dir"           "dfc.dir"          
#> [16] "cfac.dir"          "initc.dir"         "lbd.dir"          
#> [19] "hbd.dir"           "dfac.dir"          "initd.dir"        
#> [22] "lbc.init"          "hbc.init"          "cfac.init"        
#> [25] "lbd.init"          "hbd.init"          "dfac.init"        
#> [28] "ukertype"          "okertype"