Classification Random Forest Learner

Calls randomForest::randomForest from package randomForest.

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

Dictionary of Learners: mlr3::mlr_learners

Author

pat-s

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForest

Methods

Public methods

• LearnerClassifRandomForest$new()

• LearnerClassifRandomForest$importance()

• LearnerClassifRandomForest$oob_error()

• LearnerClassifRandomForest$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

LearnerClassifRandomForest$new()

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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().

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Method oob_error()

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

Usage

LearnerClassifRandomForest$oob_error()

Returns

numeric(1).

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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"