mlr_learners_surv.cforest.Rd
Calls partykit::cforest from package partykit.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.cforest") lrn("surv.cforest")
Packages: partykit, sandwich, coin
Predict Types: distr, crank
Feature Types: integer, numeric, factor, ordered
Properties: weights
Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html
Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651–674. doi: 10.1198/106186006x133933
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCForest
new()
Creates a new instance of this R6 class.
LearnerSurvCForest$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvCForest$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.cforest")) print(learner)#> <LearnerSurvCForest:surv.cforest> #> * Model: - #> * Parameters: teststat=quadratic, testtype=Univariate, mincriterion=0, #> saveinfo=FALSE #> * Packages: partykit, sandwich, coin #> * Predict Type: distr #> * Feature types: integer, numeric, factor, ordered #> * Properties: weights# available parameters: learner$param_set$ids()#> [1] "ntree" "replace" "fraction" "mtry" #> [5] "applyfun" "cores" "trace" "offset" #> [9] "cluster" "na.action" "scores" "teststat" #> [13] "splitstat" "splittest" "testtype" "nmax" #> [17] "alpha" "mincriterion" "logmincriterion" "minsplit" #> [21] "minbucket" "minprob" "stump" "lookahead" #> [25] "MIA" "nresample" "tol" "maxsurrogate" #> [29] "numsurrogate" "maxdepth" "multiway" "splittry" #> [33] "intersplit" "majority" "caseweights" "saveinfo" #> [37] "update" "splitflavour" "maxvar" "OOB" #> [41] "simplify" "scale" "nperm" "risk" #> [45] "conditional" "threshold" "maxpts" "abseps" #> [49] "releps"