mlr_learners_regr.cforest: Regression Conditional Random Forest Learner

In mlr3learners/mlr3learners.partykit: Learners from partykit package for 'mlr3'

Description

Regression conditional random forest learner. Calls partykit::cforest() from package partykit.

Dictionary

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

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

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCForest

Methods

Public methods

• LearnerRegrCForest$new()

• LearnerRegrCForest$oob_error()

• LearnerRegrCForest$clone()

Inherited methods

• 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

LearnerRegrCForest$new()

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Method oob_error()

The out-of-bag error, calculated using the OOB predictions from partykit.

Usage

LearnerRegrCForest$oob_error()

Returns

numeric(1).

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

mlr3learners.partykitpartykit1 mlr3learners.partykitpartykit2

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

if (requireNamespace("partykit")) {
  learner = mlr3::lrn("regr.cforest")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

mlr3learners/mlr3learners.partykit documentation built on June 4, 2020, 8:16 p.m.