Ranger Classification Learner

Random classification forest. Calls ranger::ranger() from package ranger.

Dictionary

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

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

References

Wright MN, Ziegler A (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1--17. doi: 10.18637/jss.v077.i01 .

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

See also

Dictionary of Learners: mlr3::mlr_learners

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRanger

Methods

Public methods

• LearnerClassifRanger$new()

• LearnerClassifRanger$importance()

• LearnerClassifRanger$oob_error()

• LearnerClassifRanger$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

LearnerClassifRanger$new()

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Method importance()

The importance scores are extracted from the model slot variable.importance. Parameter importance.mode must be set to "impurity", "impurity_corrected", or "permutation"

Usage

LearnerClassifRanger$importance()

Returns

Named numeric().

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Method oob_error()

The out-of-bag error, extracted from model slot prediction.error.

Usage

LearnerClassifRanger$oob_error()

Returns

numeric(1).

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifRanger$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("classif.ranger")
print(learner)
#> <LearnerClassifRanger:classif.ranger>
#> * Model: -
#> * Parameters: list()
#> * Packages: ranger
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, character, factor, ordered
#> * Properties: importance, multiclass, oob_error, twoclass, weights

# available parameters:
learner$param_set$ids()
#>  [1] "num.trees"                    "mtry"                        
#>  [3] "importance"                   "write.forest"                
#>  [5] "min.node.size"                "replace"                     
#>  [7] "sample.fraction"              "class.weights"               
#>  [9] "splitrule"                    "num.random.splits"           
#> [11] "split.select.weights"         "always.split.variables"      
#> [13] "respect.unordered.factors"    "scale.permutation.importance"
#> [15] "keep.inbag"                   "holdout"                     
#> [17] "num.threads"                  "save.memory"                 
#> [19] "verbose"                      "oob.error"