mlr_learners_surv.ctree: Survival Conditional Inference Tree Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival Conditional Inference Tree Learner

Description

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

Prediction types

This learner returns two prediction types:

1. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal partykit::predict.party() function.

2. crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.ctree")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, partykit, coin, sandwich

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	-
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)
maxvar	integer	-		[1, \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)

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCTree

Methods

Public methods

• LearnerSurvCTree$new()

• LearnerSurvCTree$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

LearnerSurvCTree$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

adibender

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. \Sexpr[results=rd]{tools:::Rd_expr_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.

Examples

# Define the Learner
learner = mlr3::lrn("surv.ctree")
print(learner)

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

# 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)


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

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.