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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

IdTypeDefaultLevelsRange
trialsinteger1\([1, \infty)\)
ruleslogicalFALSETRUE, FALSE-
costsuntypedNULL-
subsetlogicalTRUETRUE, FALSE-
bandsinteger-\([0, 1000]\)
winnowlogicalFALSETRUE, FALSE-
noGlobalPruninglogicalFALSETRUE, FALSE-
CFnumeric0.25\([0, 1]\)
minCasesinteger2\([0, \infty)\)
fuzzyThresholdlogicalFALSETRUE, FALSE-
samplenumeric0\([0, 0.999]\)
seedinteger-\((-\infty, \infty)\)
earlyStoppinglogicalTRUETRUE, FALSE-
labeluntyped"outcome"-
na.actionuntyped"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

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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.