Classification C5.0 Learner

Decision Tree Algorithm. Calls C50::C5.0.formula() from C50.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.C50")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, C50

Parameters

Id	Type	Default	Levels	Range
trials	integer	1		\([1, \infty)\)
rules	logical	FALSE	TRUE, FALSE	-
costs	untyped	NULL		-
subset	logical	TRUE	TRUE, FALSE	-
bands	integer	-		\([0, 1000]\)
winnow	logical	FALSE	TRUE, FALSE	-
noGlobalPruning	logical	FALSE	TRUE, FALSE	-
CF	numeric	0.25		\([0, 1]\)
minCases	integer	2		\([0, \infty)\)
fuzzyThreshold	logical	FALSE	TRUE, FALSE	-
sample	numeric	0		\([0, 0.999]\)
seed	integer	-		\((-\infty, \infty)\)
earlyStopping	logical	TRUE	TRUE, FALSE	-
label	untyped	"outcome"		-
na.action	untyped	"stats::na.pass"		-

References

Quinlan, Ross J (2014). C4. 5: programs for machine learning. Elsevier.

Author

henrifnk

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifC50

Methods

Public methods

• LearnerClassifC50$new()

• LearnerClassifC50$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifC50$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifC50$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.