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Calls extraTrees::extraTrees from package extraTrees.

Dictionary

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

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

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3extralearners, extraTrees

Parameters

IdTypeDefaultLevelsRange
ntreeinteger500\([1, \infty)\)
mtryinteger-\([1, \infty)\)
nodesizeinteger1\([1, \infty)\)
numRandomCutsinteger1\((-\infty, \infty)\)
evenCutslogicalFALSETRUE, FALSE\((-\infty, \infty)\)
numThreadsinteger1\([1, \infty)\)
subsetSizeslist-\((-\infty, \infty)\)
subsetGroupslist-\((-\infty, \infty)\)
taskslist-\((-\infty, \infty)\)
probOfTaskCutsnumeric-\([0, 1]\)
numRandomTaskCutsinteger1\([1, \infty)\)
na.actioncharacterstopstop, zero, fuse\((-\infty, \infty)\)

See also

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifExtraTrees

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifExtraTrees$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClassifExtraTrees:classif.extratrees>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, extraTrees
#> * Predict Type: response
#> * Feature types: integer, numeric
#> * Properties: multiclass, twoclass, weights
#>  [1] "ntree"             "mtry"              "nodesize"         
#>  [4] "numRandomCuts"     "evenCuts"          "numThreads"       
#>  [7] "subsetSizes"       "subsetGroups"      "tasks"            
#> [10] "probOfTaskCuts"    "numRandomTaskCuts" "na.action"