Dictionary

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

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

Traits

  • Packages: randomForest

  • Predict Types: response

  • Feature Types: integer, numeric, factor, ordered

  • Properties: importance, oob_error, weights

References

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

See also

Author

pat-s

Super classes

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

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomForest$new()


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().


Method oob_error()

OOB errors are extracted from the model slot mse.

Usage

LearnerRegrRandomForest$oob_error()

Returns

numeric(1).


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.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.randomForest")) print(learner)
#> <LearnerRegrRandomForest:regr.randomForest> #> * Model: - #> * Parameters: list() #> * Packages: randomForest #> * Predict Type: response #> * Feature types: integer, numeric, factor, ordered #> * Properties: importance, oob_error, weights
# available parameters: learner$param_set$ids()
#> [1] "ntree" "mtry" "replace" "strata" "sampsize" #> [6] "nodesize" "maxnodes" "importance" "localImp" "proximity" #> [11] "oob.prox" "norm.votes" "do.trace" "keep.forest" "keep.inbag" #> [16] "predict.all" "nodes"