mlr_learners_regr.rfsrc.Rd
Calls randomForestSRC::rfsrc from package randomForestSRC.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("regr.rfsrc") lrn("regr.rfsrc")
Packages: randomForestSRC
Predict Types: response
Feature Types: logical, integer, numeric, factor
Properties: importance, missings, oob_error, weights
Breiman L (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi: 10.1023/A:1010933404324
RaphaelS1
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrRandomForestSRC
new()
Creates a new instance of this R6 class.
LearnerRegrRandomForestSRC$new()
importance()
The importance scores are extracted from the model slot importance
.
LearnerRegrRandomForestSRC$importance()
Named numeric()
.
selected_features()
Selected features are extracted from the model slot var.used
.
LearnerRegrRandomForestSRC$selected_features()
oob_error()
OOB error extracted from the model slot err.rate
.
LearnerRegrRandomForestSRC$oob_error()
clone()
The objects of this class are cloneable with this method.
LearnerRegrRandomForestSRC$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.rfsrc")) print(learner)#> <LearnerRegrRandomForestSRC:regr.rfsrc> #> * Model: - #> * Parameters: list() #> * Packages: randomForestSRC #> * Predict Type: response #> * Feature types: logical, integer, numeric, factor #> * Properties: importance, missings, oob_error, weights# available parameters: learner$param_set$ids()#> [1] "ntree" "mtry" "nodesize" "nodedepth" "splitrule" #> [6] "nsplit" "importance" "block.size" "ensemble" "bootstrap" #> [11] "samptype" "samp" "membership" "sampsize" "na.action" #> [16] "nimpute" "ntime" "cause" "proximity" "distance" #> [21] "forest.wt" "xvar.wt" "split.wt" "forest" "var.used" #> [26] "split.depth" "seed" "do.trace" "statistics" "get.tree" #> [31] "outcome" "ptn.count"