Density Mixed Data Kernel Learner
mlr_learners_dens.mixed.Rd
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()
:
$get("dens.mixed")
mlr_learnerslrn("dens.mixed")
Meta Information
Task type: “dens”
Predict Types: “pdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, np
Parameters
Id | Type | Default | Levels | Range |
bws | untyped | - | - | |
ckertype | character | gaussian | gaussian, epanechnikov, uniform | - |
bwscaling | logical | FALSE | TRUE, FALSE | - |
bwmethod | character | cv.ml | cv.ml, cv.ls, normal-reference | - |
bwtype | character | fixed | fixed, generalized_nn, adaptive_nn | - |
bandwidth.compute | logical | FALSE | TRUE, FALSE | - |
ckerorder | integer | 2 | \([2, 8]\) | |
remin | logical | TRUE | TRUE, FALSE | - |
itmax | integer | 10000 | \([1, \infty)\) | |
nmulti | integer | - | \([1, \infty)\) | |
ftol | numeric | 1.490116e-07 | \((-\infty, \infty)\) | |
tol | numeric | 0.0001490116 | \((-\infty, \infty)\) | |
small | numeric | 1.490116e-05 | \((-\infty, \infty)\) | |
lbc.dir | numeric | 0.5 | \((-\infty, \infty)\) | |
dfc.dir | numeric | 0.5 | \((-\infty, \infty)\) | |
cfac.dir | untyped | * , 2.5 , (3 - sqrt(5)) | - | |
initc.dir | numeric | 1 | \((-\infty, \infty)\) | |
lbd.dir | numeric | 0.1 | \((-\infty, \infty)\) | |
hbd.dir | numeric | 1 | \((-\infty, \infty)\) | |
dfac.dir | untyped | * , 0.25 , (3 - sqrt(5)) | - | |
initd.dir | numeric | 1 | \((-\infty, \infty)\) | |
lbc.init | numeric | 0.1 | \((-\infty, \infty)\) | |
hbc.init | numeric | 2 | \((-\infty, \infty)\) | |
cfac.init | numeric | 0.5 | \((-\infty, \infty)\) | |
lbd.init | numeric | 0.1 | \((-\infty, \infty)\) | |
hbd.init | numeric | 0.9 | \((-\infty, \infty)\) | |
dfac.init | numeric | 0.37 | \((-\infty, \infty)\) | |
ukertype | character | - | aitchisonaitken, liracine | - |
okertype | character | - | 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
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.
Super classes
mlr3::Learner
-> mlr3proba::LearnerDens
-> LearnerDensMixed
Examples
learner = mlr3::lrn("dens.mixed")
print(learner)
#> <LearnerDensMixed:dens.mixed>: Kernel Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, np
#> * Predict Types: [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"