Gradient Boosting with Regression Trees Survival Learner

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		-

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.

Author

RaphaelS1

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.

Examples

learner = mlr3::lrn("surv.blackboost")
print(learner)
#> <LearnerSurvBlackBoost:surv.blackboost>: Gradient Boosting
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
#> * Predict Types:  crank, [distr], lp
#> * Feature Types: integer, numeric, factor
#> * Properties: weights

# available parameters:
learner$param_set$ids()
#>  [1] "family"          "custom.family"   "nuirange"        "offset"         
#>  [5] "center"          "mstop"           "nu"              "risk"           
#>  [9] "stopintern"      "trace"           "oobweights"      "teststat"       
#> [13] "splitstat"       "splittest"       "testtype"        "maxpts"         
#> [17] "abseps"          "releps"          "nmax"            "alpha"          
#> [21] "mincriterion"    "logmincriterion" "minsplit"        "minbucket"      
#> [25] "minprob"         "stump"           "lookahead"       "MIA"            
#> [29] "nresample"       "tol"             "maxsurrogate"    "mtry"           
#> [33] "maxdepth"        "multiway"        "splittry"        "intersplit"     
#> [37] "majority"        "caseweights"     "sigma"           "ipcw"           
#> [41] "na.action"