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

Format

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

Construction

LearnerRegrRanger$new()
mlr3::mlr_learners$get("regr.ranger")
mlr3::lrn("regr.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

Examples

learner = mlr3::lrn("regr.ranger") print(learner)
#> <LearnerRegrRanger:regr.ranger> #> * Model: - #> * Parameters: list() #> * Packages: ranger #> * Predict Type: response #> * Feature types: logical, integer, numeric, character, factor, ordered #> * Properties: importance, oob_error, weights
# available parameters: learner$param_set$ids()
#> [1] "num.trees" "mtry" #> [3] "importance" "write.forest" #> [5] "min.node.size" "replace" #> [7] "sample.fraction" "splitrule" #> [9] "num.random.splits" "split.select.weights" #> [11] "always.split.variables" "respect.unordered.factors" #> [13] "scale.permutation.importance" "keep.inbag" #> [15] "holdout" "num.threads" #> [17] "save.memory" "verbose" #> [19] "oob.error"