mlr_learners_dens.locfit: Density Locfit Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Density Locfit Learner

Description

Local density estimation. Calls locfit::density.lf() from locfit.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.locfit")

Meta Information

• Task type: “dens”

• Predict Types: “pdf”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, locfit

Parameters

Id	Type	Default	Levels	Range
window	character	gaus	tcub, rect, trwt, tria, epan, bisq, gaus	-
width	numeric	-		(-\infty, \infty)
from	numeric	-		(-\infty, \infty)
to	numeric	-		(-\infty, \infty)
cut	numeric	-		(-\infty, \infty)
deg	numeric	0		(-\infty, \infty)
link	character	ident	ident, log, logit, inverse, sqrt, arcsin	-
kern	character	tcub	rect, trwt, tria, epan, bisq, gauss, tcub	-
kt	character	sph	sph, prod	-
renorm	logical	FALSE	TRUE, FALSE	-
maxk	integer	100		[0, \infty)
itype	character	-	prod, mult, mlin, haz	-
mint	integer	20		[1, \infty)
maxit	integer	20		[1, \infty)

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLocfit

Methods

Public methods

• LearnerDensLocfit$new()

• LearnerDensLocfit$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensLocfit$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensLocfit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Loader, Clive (2006). Local regression and likelihood. Springer Science & Business Media.

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.locfit")
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.