mlr_learners_dens.spline: Density Smoothing Splines Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Density Smoothing Splines Learner

Description

Density Smoothing Splines Learner. Calls gss::ssden() from gss.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.spline")

Meta Information

• Task type: “dens”

• Predict Types: “pdf”, “cdf”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, gss

Parameters

Id	Type	Default	Levels	Range
type	untyped	-		-
alpha	numeric	1.4		(-\infty, \infty)
weights	untyped	-		-
na.action	untyped	stats::na.omit		-
id.basis	untyped	-		-
nbasis	integer	-		(-\infty, \infty)
seed	numeric	-		(-\infty, \infty)
domain	untyped	-		-
quad	untyped	-		-
qdsz.depth	numeric	-		(-\infty, \infty)
bias	untyped	-		-
prec	numeric	1e-07		(-\infty, \infty)
maxiter	integer	30		[1, \infty)
skip.iter	logical	-	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensSpline

Methods

Public methods

• LearnerDensSpline$new()

• LearnerDensSpline$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

LearnerDensSpline$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensSpline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Gu, Chong, Wang, Jingyuan (2003). “Penalized likelihood density estimation: Direct cross-validation and scalable approximation.” Statistica Sinica, 811–826.

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.spline")
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 Nov. 11, 2024, 11:11 a.m.