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