Ranger Classification Learner

Source: R/LearnerClassifRanger.R

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

Format

R6::R6Class() inheriting from mlr3::LearnerClassif.

Construction

LearnerClassifRanger$new()
mlr3::mlr_learners$get("classif.ranger")
mlr3::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

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"