mlr_learners_regr.randomForest.Rd
Calls randomForest::randomForest from package randomForest.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("regr.randomForest") lrn("regr.randomForest")
Packages: randomForest
Predict Types: response
Feature Types: integer, numeric, factor, ordered
Properties: importance, oob_error, weights
Breiman, L. (2001). Random Forests Machine Learning https://doi.org/10.1023/A:1010933404324
pat-s
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrRandomForest
new()
Creates a new instance of this R6 class.
LearnerRegrRandomForest$new()
importance()
The importance scores are extracted from the slot importance
.
Parameter 'importance' must be set to either "mse"
or "nodepurity"
.
LearnerRegrRandomForest$importance()
Named numeric()
.
oob_error()
OOB errors are extracted from the model slot mse
.
LearnerRegrRandomForest$oob_error()
clone()
The objects of this class are cloneable with this method.
LearnerRegrRandomForest$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.randomForest")) print(learner)#> <LearnerRegrRandomForest:regr.randomForest> #> * Model: - #> * Parameters: list() #> * Packages: randomForest #> * Predict Type: response #> * Feature types: integer, numeric, factor, ordered #> * Properties: importance, oob_error, weights# available parameters: learner$param_set$ids()#> [1] "ntree" "mtry" "replace" "strata" "sampsize" #> [6] "nodesize" "maxnodes" "importance" "localImp" "proximity" #> [11] "oob.prox" "norm.votes" "do.trace" "keep.forest" "keep.inbag" #> [16] "predict.all" "nodes"