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Kernel density estimation with global bandwidth selection via "plug-in". Calls plugdensity::plugin.density() from plugdensity.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.plug")

Meta Information

Parameters

IdTypeDefaultLevels
na.rmlogicalFALSETRUE, FALSE

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

Author

RaphaelS1

Super classes

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

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

LearnerDensPlugin$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("dens.plug")
print(learner)
#> <LearnerDensPlugin:dens.plug>: Kernel Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, plugdensity
#> * Predict Types:  [pdf]
#> * Feature Types: numeric
#> * Properties: missings

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::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 
#>     0.992556