Survival Conditional Random Forest Learner

A random forest based on conditional inference trees (ctree). Calls partykit::cforest() from partykit.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.cforest")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

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

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

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		\([1, \infty)\)
replace	logical	FALSE	TRUE, FALSE	-
fraction	numeric	0.632		\([0, 1]\)
mtry	integer	-		\([0, \infty)\)
mtryratio	numeric	-		\([0, 1]\)
applyfun	untyped	-		-
cores	integer	NULL		\((-\infty, \infty)\)
trace	logical	FALSE	TRUE, FALSE	-
offset	untyped	-		-
cluster	untyped	-		-
na.action	untyped	"stats::na.pass"		-
scores	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Univariate	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
alpha	numeric	0.05		\([0, 1]\)
mincriterion	numeric	0.95		\([0, 1]\)
logmincriterion	numeric	-0.05129329		\((-\infty, \infty)\)
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	1.490116e-08		\([0, \infty)\)
maxsurrogate	integer	0		\([0, \infty)\)
numsurrogate	logical	FALSE	TRUE, FALSE	-
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	TRUE	TRUE, FALSE	-
saveinfo	logical	FALSE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
maxvar	integer	-		\([1, \infty)\)
OOB	logical	FALSE	TRUE, FALSE	-
simplify	logical	TRUE	TRUE, FALSE	-
scale	logical	TRUE	TRUE, FALSE	-
maxpts	integer	25000		\((-\infty, \infty)\)
abseps	numeric	0.001		\([0, \infty)\)
releps	numeric	0		\([0, \infty)\)

Custom mlr3 parameters

• mtry:

  • This hyperparameter can alternatively be set via the added hyperparameter mtryratio as mtry = max(ceiling(mtryratio * n_features), 1). Note that mtry and mtryratio are mutually exclusive.

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

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCForest

Methods

Public methods

• LearnerSurvCForest$new()

• LearnerSurvCForest$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()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCForest$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCForest$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

task = tsk("rats")
learner = lrn("surv.cforest", ntree = 50)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)