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")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3extralearners, randomForest

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		\([1, \infty)\)
mtry	integer	-		\([1, \infty)\)
replace	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
classwt	list	NULL		\((-\infty, \infty)\)
cutoff	list	-		\((-\infty, \infty)\)
strata	list	-		\((-\infty, \infty)\)
sampsize	list	-		\((-\infty, \infty)\)
nodesize	integer	1		\([1, \infty)\)
maxnodes	integer	-		\([1, \infty)\)
importance	character	FALSE	accuracy, gini, none, FALSE	\((-\infty, \infty)\)
localImp	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
proximity	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
oob.prox	logical	-	TRUE, FALSE	\((-\infty, \infty)\)
norm.votes	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
do.trace	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
keep.forest	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
keep.inbag	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
predict.all	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
nodes	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)

References

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

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

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$base_learner()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

---

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

if (requireNamespace("randomForest", quietly = TRUE)) {
  learner = mlr3::lrn("classif.randomForest")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClassifRandomForest:classif.randomForest>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, randomForest
#> * Predict Type: response
#> * Feature types: numeric, factor, ordered
#> * Properties: importance, multiclass, oob_error, twoclass, weights
#>  [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"