Skip to contents

Calls partykit::ctree from package partykit.

Dictionary

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")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3extralearners, partykit, sandwich, coin

Parameters

IdTypeDefaultLevelsRange
teststatcharacterquadraticquadratic, maximum\((-\infty, \infty)\)
splitstatcharacterquadraticquadratic, maximum\((-\infty, \infty)\)
splittestlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
testtypecharacterBonferroniBonferroni, MonteCarlo, Univariate, Teststatistic\((-\infty, \infty)\)
nmaxlist-\((-\infty, \infty)\)
alphanumeric0.05\([0, 1]\)
mincriterionnumeric0.95\([0, 1]\)
logmincriterionnumeric-\((-\infty, \infty)\)
minsplitinteger20\([1, \infty)\)
minbucketinteger7\([1, \infty)\)
minprobnumeric0.01\([0, \infty)\)
stumplogicalFALSETRUE, FALSE\((-\infty, \infty)\)
lookaheadlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
MIAlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
maxvarinteger-\([1, \infty)\)
nresampleinteger9999\([1, \infty)\)
tolnumeric-\([0, \infty)\)
maxsurrogateinteger0\([0, \infty)\)
numsurrogatelogicalFALSETRUE, FALSE\((-\infty, \infty)\)
mtryintegerInf\([0, \infty)\)
maxdepthintegerInf\([0, \infty)\)
multiwaylogicalFALSETRUE, FALSE\((-\infty, \infty)\)
splittryinteger2\([0, \infty)\)
intersplitlogicalFALSETRUE, FALSE\((-\infty, \infty)\)
majoritylogicalFALSETRUE, FALSE\((-\infty, \infty)\)
caseweightslogicalFALSETRUE, FALSE\((-\infty, \infty)\)
applyfunlist-\((-\infty, \infty)\)
coresintegerNULL\((-\infty, \infty)\)
saveinfologicalTRUETRUE, FALSE\((-\infty, \infty)\)
updatelogicalFALSETRUE, FALSE\((-\infty, \infty)\)
splitflavourcharacterctreectree, exhaustive\((-\infty, \infty)\)
offsetlist-\((-\infty, \infty)\)
clusterlist-\((-\infty, \infty)\)
scoreslist-\((-\infty, \infty)\)
doFitlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
maxptsinteger25000\((-\infty, \infty)\)
absepsnumeric0.001\([0, \infty)\)
relepsnumeric0\([0, \infty)\)

References

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

See also

Author

sumny

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCTree

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("partykit", quietly = TRUE) && requireNamespace("sandwich", quietly = TRUE) && requireNamespace("coin", quietly = TRUE)) {
  learner = mlr3::lrn("regr.ctree")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerRegrCTree:regr.ctree>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, partykit, sandwich, coin
#> * Predict Type: response
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: weights
#>  [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"