Regression Conditional Inference Tree Learner

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 lrn():

lrn("regr.ctree")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• 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		\([0, \infty)\)
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
maxvar	integer	-		\([1, \infty)\)
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	-
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

• 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$base_learner()
• mlr3::Learner$encapsulate()
• 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

# Define the Learner
learner = mlr3::lrn("regr.ctree")
print(learner)
#> <LearnerRegrCTree:regr.ctree>: Conditional Inference Tree
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, partykit, sandwich, coin
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: weights

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

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

print(learner$model)
#> 
#> Model formula:
#> mpg ~ am + carb + cyl + disp + drat + gear + hp + qsec + vs + 
#>     wt
#> 
#> Fitted party:
#> [1] root
#> |   [2] wt <= 3.15: 24.789 (n = 9, err = 118.8)
#> |   [3] wt > 3.15: 17.158 (n = 12, err = 123.0)
#> 
#> Number of inner nodes:    1
#> Number of terminal nodes: 2


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

# Score the predictions
predictions$score()
#> regr.mse 
#> 25.00913