Classification Conditional Inference Tree Learner

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 lrn():

lrn("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	-
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

• 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::LearnerClassif -> LearnerClassifCTree

Methods

Public methods

• LearnerClassifCTree$new()

• LearnerClassifCTree$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifCTree$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifCTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.ctree")
print(learner)
#> 
#> ── <LearnerClassifCTree> (classif.ctree): Conditional Inference Tree ───────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, partykit, sandwich, and coin
#> • Predict Types: [response] and prob
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass, twoclass, and weights
#> • Other settings: use_weights = 'use'

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> 
#> Model formula:
#> Class ~ V1 + V10 + V11 + V12 + V13 + V14 + V15 + V16 + V17 + 
#>     V18 + V19 + V2 + V20 + V21 + V22 + V23 + V24 + V25 + V26 + 
#>     V27 + V28 + V29 + V3 + V30 + V31 + V32 + V33 + V34 + V35 + 
#>     V36 + V37 + V38 + V39 + V4 + V40 + V41 + V42 + V43 + V44 + 
#>     V45 + V46 + V47 + V48 + V49 + V5 + V50 + V51 + V52 + V53 + 
#>     V54 + V55 + V56 + V57 + V58 + V59 + V6 + V60 + V7 + V8 + 
#>     V9
#> 
#> Fitted party:
#> [1] root
#> |   [2] V11 <= 0.168
#> |   |   [3] V1 <= 0.0231: R (n = 35, err = 8.6%)
#> |   |   [4] V1 > 0.0231: M (n = 16, err = 50.0%)
#> |   [5] V11 > 0.168: M (n = 88, err = 27.3%)
#> 
#> Number of inner nodes:    2
#> Number of terminal nodes: 3


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2898551