mlr_learners_dens.logspline: Density Logspline Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Density Logspline Learner

Description

Density logspline learner. Calls logspline::logspline() from logspline.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.logspline")

Meta Information

• Task type: “dens”

• Predict Types: “pdf”, “cdf”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, logspline

Parameters

Id	Type	Default	Levels	Range
lbound	numeric	-		(-\infty, \infty)
ubound	numeric	-		(-\infty, \infty)
maxknots	numeric	0		[0, \infty)
knots	untyped	-		-
nknots	numeric	0		[0, \infty)
penalty	untyped	-		-
silent	logical	TRUE	TRUE, FALSE	-
mind	numeric	-1		(-\infty, \infty)
error.action	integer	2		[0, 2]

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLogspline

Methods

Public methods

• LearnerDensLogspline$new()

• LearnerDensLogspline$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

LearnerDensLogspline$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensLogspline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Kooperberg, Charles, Stone, J C (1992). “Logspline density estimation for censored data.” Journal of Computational and Graphical Statistics, 1(4), 301–328.

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.

Examples

# Define the Learner
learner = mlr3::lrn("dens.logspline")
print(learner)

# 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)


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

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.