Density Plug-In Kernel Learner

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

• Task type: “dens”

• Predict Types: “pdf”

• Feature Types: “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, plugdensity

Parameters

Id	Type	Default	Levels
na.rm	logical	FALSE	TRUE, 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

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Author

RaphaelS1

Super classes

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

Methods

Public methods

• LearnerDensPlugin$new()

• LearnerDensPlugin$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• 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 = 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 
#>    0.9672473