Plug-in Kernel Density Estimator

Calls plugdensity::plugin.density() and the result is coerced to a distr6::Distribution.

Details

Kernel density estimation by "plug-in" bandwidth selection.

Dictionary

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

LearnerDensPlugin$new()
mlr_learners$get("dens.plug")
lrn("dens.plug")

Meta Information

• Type: "dens"

• Predict Types: pdf

• Feature Types: numeric

• Properties: missings

• Packages: mlr3 mlr3proba plugdensity distr6

References

Engel, Joachim, Herrmann, Eva, Gasser, Theo (1994). “An iterative bandwidth selector for kernel estimation of densities and their derivatives.” Journaltitle of Nonparametric Statistics, 4(1), 21–34.

See also

Other density estimators: mlr_learners_dens.hist, mlr_learners_dens.kde, mlr_learners_dens.kde_ks, mlr_learners_dens.locfit, mlr_learners_dens.logspline, mlr_learners_dens.mixed, mlr_learners_dens.nonpar, mlr_learners_dens.pen, mlr_learners_dens.spline

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensPlugin

Methods

Public methods

• LearnerDensPlugin$new()

• LearnerDensPlugin$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensPlugin$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensPlugin$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("dens.plug")
print(learner)
#> 
#> ── <LearnerDensPlugin> (dens.plug): Kernel Density Estimation by Plug-In Bandwid
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3proba, plugdensity, and distr6
#> • Predict Types: [pdf]
#> • Feature Types: numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: missings
#> • Other settings: use_weights = 'error'

# Define a Task
task = tsk("faithful")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> PluginKDE() 

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> dens.logloss 
#>     1.021812