Dictionary

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

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

Traits

  • Packages: randomForest

  • Predict Types: response, prob

  • Feature Types: numeric, factor, ordered

  • Properties: importance, multiclass, oob_error, twoclass, 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::LearnerClassif -> LearnerClassifRandomForest

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForest$new()


Method importance()

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

Usage

LearnerClassifRandomForest$importance()

Returns

Named numeric().


Method oob_error()

OOB errors are extracted from the model slot err.rate.

Usage

LearnerClassifRandomForest$oob_error()

Returns

numeric(1).


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForest$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("classif.randomForest")) print(learner)
#> <LearnerClassifRandomForest:classif.randomForest> #> * Model: - #> * Parameters: list() #> * Packages: randomForest #> * Predict Type: response #> * Feature types: numeric, factor, ordered #> * Properties: importance, multiclass, oob_error, twoclass, weights
# available parameters: learner$param_set$ids()
#> [1] "ntree" "mtry" "replace" "classwt" "cutoff" #> [6] "strata" "sampsize" "nodesize" "maxnodes" "importance" #> [11] "localImp" "proximity" "oob.prox" "norm.votes" "do.trace" #> [16] "keep.forest" "keep.inbag" "predict.all" "nodes"