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Calls Cubist::cubist from package Cubist.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.cubist")
lrn("regr.cubist")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

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

  • Required Packages: mlr3extralearners, Cubist

Parameters

IdTypeDefaultLevelsRange
committeesinteger1\([1, 100]\)
unbiasedlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
rulesinteger100\([1, \infty)\)
extrapolationnumeric100\([0, 100]\)
sampleinteger0\([0, \infty)\)
seedinteger3470\((-\infty, \infty)\)
labellistoutcome\((-\infty, \infty)\)
neighborsinteger0\([0, 0]\)

See also

Author

sumny

Super classes

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

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

LearnerRegrCubist$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("Cubist", quietly = TRUE)) {
  learner = mlr3::lrn("regr.cubist")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerRegrCubist:regr.cubist>
#> * Model: -
#> * Parameters: committees=1, neighbors=0
#> * Packages: mlr3, mlr3extralearners, Cubist
#> * Predict Type: response
#> * Feature types: integer, numeric, character, factor, ordered
#> * Properties: -
#> [1] "committees"    "unbiased"      "rules"         "extrapolation"
#> [5] "sample"        "seed"          "label"         "neighbors"