mlr_learners_regr.cubist: Regression Cubist Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Regression Cubist Learner

Description

Rule-based model that is an extension of Quinlan's M5 model tree. Each tree contains linear regression models at the terminal leaves. Calls Cubist::cubist() from Cubist.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.cubist")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, Cubist

Parameters

Id	Type	Default	Levels	Range
committees	integer	-		[1, 100]
unbiased	logical	FALSE	TRUE, FALSE	-
rules	integer	100		[1, \infty)
extrapolation	numeric	100		[0, 100]
sample	integer	0		[0, \infty)
seed	integer	-		(-\infty, \infty)
label	untyped	"outcome"		-
neighbors	integer	-		[0, 9]

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCubist

Methods

Public methods

• LearnerRegrCubist$new()

• LearnerRegrCubist$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

LearnerRegrCubist$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCubist$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

sumny

References

Quinlan, R J, others (1992). “Learning with continuous classes.” In 5th Australian joint conference on artificial intelligence, volume 92, 343–348. World Scientific.

Quinlan, Ross J (1993). “Combining instance-based and model-based learning.” In Proceedings of the tenth international conference on machine learning, 236–243.

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("regr.cubist")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# 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.