mlr_learners_regr.randomForest: Regression Random Forest Learner

In mlr3learners/mlr3learners.randomForest: Random forest for mlr3 (randomForest package)

Description

Random forest learner. randomForest from package randomForest.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomForest

Methods

Public methods

• LearnerRegrRandomForest$new()

• LearnerRegrRandomForest$importance()

• LearnerRegrRandomForest$oob_error()

• LearnerRegrRandomForest$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

LearnerRegrRandomForest$new()

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Method importance()

The importance scores are extracted from the slot importance. Parameter 'importance' must be set to either "mse" or "nodepurity".

Usage

LearnerRegrRandomForest$importance()

Returns

Named numeric().

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Method oob_error()

OOB errors are extracted from the model slot mse.

Usage

LearnerRegrRandomForest$oob_error()

Returns

numeric(1).

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Breiman, L. (2001). Random Forests Machine Learning https://doi.org/10.1023/A:1010933404324

mlr3learners/mlr3learners.randomForest documentation built on June 4, 2020, 9:21 a.m.