mlr_learners_surv.blackboost: Gradient Boosting with Regression Trees Survival Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Gradient Boosting with Regression Trees Survival Learner

Description

Gradient boosting with regression trees for survival analysis. Calls mboost::blackboost() from mboost.

Details

distr prediction made by mboost::survFit().

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("surv.blackboost")
lrn("surv.blackboost")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”, “lp”

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma

Parameters

Id	Type	Default	Levels	Range
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
offset	untyped	-		-
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		[0, \infty)
nu	numeric	0.1		[0, 1]
risk	character	-	inbag, oobag, none	-
stopintern	logical	FALSE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
oobweights	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
maxpts	integer	25000		[1, \infty)
abseps	numeric	0.001		(-\infty, \infty)
releps	numeric	0		(-\infty, \infty)
nmax	untyped	-		-
alpha	numeric	0.05		[0, 1]
mincriterion	numeric	0.95		[0, 1]
logmincriterion	numeric	-0.05129329		(-\infty, 0]
minsplit	integer	20		[0, \infty)
minbucket	integer	7		[0, \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)
mtry	integer	-		[0, \infty)
maxdepth	integer	-		[0, \infty)
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		[1, \infty)
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
sigma	numeric	0.1		[0, 1]
ipcw	untyped	1		-
na.action	untyped	stats::na.omit		-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvBlackBoost

Methods

Public methods

• LearnerSurvBlackBoost$new()

• LearnerSurvBlackBoost$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvBlackBoost$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvBlackBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

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

learner = mlr3::lrn("surv.blackboost")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.