Gradient Boosting with Regression Trees Survival Learner

Calls mboost::blackboost from package 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: “distr”, “crank”, “lp”

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

• Required Packages: mlr3extralearners, mboost, pracma

Parameters

Id	Type	Default	Levels	Range
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	\((-\infty, \infty)\)
custom.family	list	-		\((-\infty, \infty)\)
nuirange	list	0, 100		\((-\infty, \infty)\)
offset	list	-		\((-\infty, \infty)\)
center	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
mstop	integer	100		\([0, \infty)\)
nu	numeric	0.1		\([0, 1]\)
risk	character	-	inbag, oobag, none	\((-\infty, \infty)\)
stopintern	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
trace	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
oobweights	list	-		\((-\infty, \infty)\)
teststat	character	quadratic	quadratic, maximum	\((-\infty, \infty)\)
splitstat	character	quadratic	quadratic, maximum	\((-\infty, \infty)\)
splittest	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	\((-\infty, \infty)\)
maxpts	integer	25000		\([1, \infty)\)
abseps	numeric	0.001		\((-\infty, \infty)\)
releps	numeric	0		\((-\infty, \infty)\)
nmax	list	-		\((-\infty, \infty)\)
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	\((-\infty, \infty)\)
lookahead	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
MIA	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
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	\((-\infty, \infty)\)
splittry	integer	2		\([1, \infty)\)
intersplit	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
majority	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
caseweights	logical	TRUE	TRUE, FALSE	\((-\infty, \infty)\)
sigma	numeric	0.1		\([0, 1]\)
ipcw	list	1		\((-\infty, \infty)\)
na.action	list	function (object, ...) , UseMethod("na.omit")		\((-\infty, \infty)\)

References

Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324–339. doi: 10.1198/016214503000125

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

if (requireNamespace("mboost", quietly = TRUE) && requireNamespace("pracma", quietly = TRUE)) {
  learner = mlr3::lrn("surv.blackboost")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerSurvBlackBoost:surv.blackboost>
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3extralearners, mboost, pracma
#> * Predict Type: distr
#> * Feature types: integer, numeric, factor
#> * Properties: weights
#>  [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"