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Random forest for regression. Calls randomForest::randomForest() from randomForest.

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")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, mlr3extralearners, randomForest

Parameters

IdTypeDefaultLevelsRange
ntreeinteger500\([1, \infty)\)
mtryinteger-\([1, \infty)\)
replacelogicalTRUETRUE, FALSE-
stratauntyped--
sampsizeuntyped--
nodesizeinteger5\([1, \infty)\)
maxnodesinteger-\([1, \infty)\)
importancecharacterFALSEmse, nudepurity, none, FALSE-
localImplogicalFALSETRUE, FALSE-
proximitylogicalFALSETRUE, FALSE-
oob.proxlogical-TRUE, FALSE-
norm.voteslogicalTRUETRUE, FALSE-
do.tracelogicalFALSETRUE, FALSE-
keep.forestlogicalTRUETRUE, FALSE-
keep.inbaglogicalFALSETRUE, FALSE-
predict.alllogicalFALSETRUE, FALSE-
nodeslogicalFALSETRUE, FALSE-

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5--32. ISSN 1573-0565, doi:10.1023/A:1010933404324 .

See also

Author

pat-s

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


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

learner = mlr3::lrn("regr.randomForest")
print(learner)
#> <LearnerRegrRandomForest:regr.randomForest>: Random Forest
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, 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"