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