Classification Conditional Inference Tree Learner
mlr_learners_classif.ctree.Rd
Classification 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()
:
$get("classif.ctree")
mlr_learnerslrn("classif.ctree")
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin
Parameters
Id | Type | Default | Levels | Range |
teststat | character | quadratic | quadratic, maximum | - |
splitstat | character | quadratic | quadratic, maximum | - |
splittest | logical | FALSE | TRUE, FALSE | - |
testtype | character | Bonferroni | Bonferroni, MonteCarlo, Univariate, Teststatistic | - |
nmax | untyped | - | - | |
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 | \([0, 1]\) | |
stump | logical | FALSE | TRUE, FALSE | - |
lookahead | logical | FALSE | TRUE, FALSE | - |
MIA | logical | FALSE | TRUE, FALSE | - |
nresample | integer | 9999 | \([1, \infty)\) | |
tol | numeric | - | \([0, \infty)\) | |
maxsurrogate | integer | 0 | \([0, \infty)\) | |
numsurrogate | logical | FALSE | TRUE, FALSE | - |
mtry | integer | Inf | \([0, \infty)\) | |
maxdepth | integer | Inf | \([0, \infty)\) | |
multiway | logical | FALSE | TRUE, FALSE | - |
splittry | integer | 2 | \([0, \infty)\) | |
intersplit | logical | FALSE | TRUE, FALSE | - |
majority | logical | FALSE | TRUE, FALSE | - |
caseweights | logical | FALSE | TRUE, FALSE | - |
maxvar | integer | - | \([1, \infty)\) | |
applyfun | untyped | - | - | |
cores | integer | NULL | \((-\infty, \infty)\) | |
saveinfo | logical | TRUE | TRUE, FALSE | - |
update | logical | FALSE | TRUE, FALSE | - |
splitflavour | character | ctree | ctree, exhaustive | - |
offset | untyped | - | - | |
cluster | untyped | - | - | |
scores | untyped | - | - | |
doFit | logical | TRUE | TRUE, FALSE | - |
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 , https://doi.org/10.1198/106186006x133933.
See also
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.
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifCTree
Examples
learner = mlr3::lrn("classif.ctree")
print(learner)
#> <LearnerClassifCTree:classif.ctree>: Conditional Inference Tree
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, partykit, sandwich, coin
#> * Predict Types: [response], prob
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: multiclass, twoclass, 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] "multiway" "splittry" "intersplit" "majority"
#> [25] "caseweights" "maxvar" "applyfun" "cores"
#> [29] "saveinfo" "update" "splitflavour" "offset"
#> [33] "cluster" "scores" "doFit" "maxpts"
#> [37] "abseps" "releps"