mlr_learners_dens.kde_ks: Density KS Kernel Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Density KS Kernel Learner

Description

Kernel density estimator. Calls ks::kde() from ks.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.kde_ks")

Meta Information

• Task type: “dens”

• Predict Types: “pdf”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, ks

Parameters

Id	Type	Default	Levels	Range
h	numeric	-		[0, \infty)
H	untyped	-		-
gridsize	untyped	-		-
gridtype	untyped	-		-
xmin	numeric	-		(-\infty, \infty)
xmax	numeric	-		(-\infty, \infty)
supp	numeric	3.7		(-\infty, \infty)
binned	numeric	-		(-\infty, \infty)
bgridsize	untyped	-		-
positive	logical	FALSE	TRUE, FALSE	-
adj.positive	untyped	-		-
w	untyped	-		-
compute.cont	logical	TRUE	TRUE, FALSE	-
approx.cont	logical	TRUE	TRUE, FALSE	-
unit.interval	logical	FALSE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
density	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensKDEks

Methods

Public methods

• LearnerDensKDEks$new()

• LearnerDensKDEks$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensKDEks$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensKDEks$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Gramacki, Artur, Gramacki, Jarosław (2017). “FFT-based fast computation of multivariate kernel density estimators with unconstrained bandwidth matrices.” Journal of Computational and Graphical Statistics, 26(2), 459–462.

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.kde_ks")
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 Sept. 16, 2024, 3:11 a.m.