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Regression Partition Tree where a significance test is used to determine the univariate splits. Calls partykit::ctree() from 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

Parameters

IdTypeDefaultLevelsRange
teststatcharacterquadraticquadratic, maximum-
splitstatcharacterquadraticquadratic, maximum-
splittestlogicalFALSETRUE, FALSE-
testtypecharacterBonferroniBonferroni, MonteCarlo, Univariate, Teststatistic-
nmaxuntyped--
alphanumeric0.05\([0, 1]\)
mincriterionnumeric0.95\([0, 1]\)
logmincriterionnumeric-\((-\infty, \infty)\)
minsplitinteger20\([1, \infty)\)
minbucketinteger7\([1, \infty)\)
minprobnumeric0.01\([0, \infty)\)
stumplogicalFALSETRUE, FALSE-
lookaheadlogicalFALSETRUE, FALSE-
MIAlogicalFALSETRUE, FALSE-
maxvarinteger-\([1, \infty)\)
nresampleinteger9999\([1, \infty)\)
tolnumeric-\([0, \infty)\)
maxsurrogateinteger0\([0, \infty)\)
numsurrogatelogicalFALSETRUE, FALSE-
mtryintegerInf\([0, \infty)\)
maxdepthintegerInf\([0, \infty)\)
multiwaylogicalFALSETRUE, FALSE-
splittryinteger2\([0, \infty)\)
intersplitlogicalFALSETRUE, FALSE-
majoritylogicalFALSETRUE, FALSE-
caseweightslogicalFALSETRUE, FALSE-
applyfununtyped--
coresintegerNULL\((-\infty, \infty)\)
saveinfologicalTRUETRUE, FALSE-
updatelogicalFALSETRUE, FALSE-
splitflavourcharacterctreectree, exhaustive-
offsetuntyped--
clusteruntyped--
scoresuntyped--
doFitlogicalTRUETRUE, FALSE-
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 , https://doi.org/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

learner = mlr3::lrn("regr.ctree")
print(learner)
#> <LearnerRegrCTree:regr.ctree>: Conditional Inference Tree
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, 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"