Density Penalized Learner

Density estimation using penalized B-splines with automatic selection of smoothing parameter. Calls pendensity::pendensity() from pendensity.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.pen")

Meta Information

• Task type: “dens”

• Predict Types: “pdf”, “cdf”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, pendensity

Parameters

Id	Type	Default	Levels	Range
base	character	bspline	bspline, gaussian	-
no.base	numeric	41		\((-\infty, \infty)\)
max.iter	numeric	20		\((-\infty, \infty)\)
lambda0	numeric	500		\((-\infty, \infty)\)
q	numeric	3		\((-\infty, \infty)\)
sort	logical	TRUE	TRUE, FALSE	-
with.border	untyped	-		-
m	numeric	3		\((-\infty, \infty)\)
eps	numeric	0.01		\((-\infty, \infty)\)

References

Schellhase, Christian, Kauermann, Göran (2012). “Density estimation and comparison with a penalized mixture approach.” Computational Statistics, 27(4), 757–777.

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 -> LearnerDensPenalized

Methods

Public methods

• LearnerDensPenalized$new()

• LearnerDensPenalized$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

LearnerDensPenalized$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensPenalized$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = mlr3::lrn("dens.pen")
print(learner)
#> 
#> ── <LearnerDensPenalized> (dens.pen): Penalized Density Estimation ─────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3proba, mlr3extralearners, and pendensity
#> • Predict Types: [pdf] and cdf
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error'

# 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)
#> [1] 1
#> [1] 2
#> [1] 3

print(learner$model)
#> PenDens_gaussian() 


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

# Score the predictions
predictions$score()
#> dens.logloss 
#>     2.369786