Regression Conditional Inference Tree Learner

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

Id	Type	Default	Levels	Range
teststat	character	quadratic	quadratic, maximum	\((-\infty, \infty)\)
splitstat	character	quadratic	quadratic, maximum	\((-\infty, \infty)\)
splittest	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	\((-\infty, \infty)\)
nmax	list	-		\((-\infty, \infty)\)
alpha	numeric	0.05		\([0, 1]\)
mincriterion	numeric	0.95		\([0, 1]\)
logmincriterion	numeric	-		\((-\infty, \infty)\)
minsplit	integer	20		\([1, \infty)\)
minbucket	integer	7		\([1, \infty)\)
minprob	numeric	0.01		\([0, \infty)\)
stump	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
lookahead	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
MIA	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
maxvar	integer	-		\([1, \infty)\)
nresample	integer	9999		\([1, \infty)\)
tol	numeric	-		\([0, \infty)\)
maxsurrogate	integer	0		\([0, \infty)\)
numsurrogate	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
mtry	integer	Inf		\([0, \infty)\)
maxdepth	integer	Inf		\([0, \infty)\)
multiway	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
splittry	integer	2		\([0, \infty)\)
intersplit	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
majority	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
caseweights	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
applyfun	list	-		\((-\infty, \infty)\)
cores	integer	NULL		\((-\infty, \infty)\)
saveinfo	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
update	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
splitflavour	character	ctree	ctree, exhaustive	\((-\infty, \infty)\)
offset	list	-		\((-\infty, \infty)\)
cluster	list	-		\((-\infty, \infty)\)
scores	list	-		\((-\infty, \infty)\)
doFit	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
maxpts	integer	25000		\((-\infty, \infty)\)
abseps	numeric	0.001		\([0, \infty)\)
releps	numeric	0		\([0, \infty)\)

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

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

sumny

Super classes

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

Methods

Public methods

• LearnerRegrCTree$new()

• LearnerRegrCTree$clone()

Inherited methods

• mlr3::Learner$base_learner()

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrCTree$new()

---

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"