Boosted Generalized Additive Survival Learner

Model-based boosting for survival analysis. Calls mboost::mboost() 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.mboost")
lrn("surv.mboost")

Meta Information

• Task type: “surv”

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

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

• Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost

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	numeric	-		\((-\infty, \infty)\)
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		\([0, \infty)\)
nu	numeric	0.1		\([0, 1]\)
risk	character	inbag	inbag, oobag, none	-
stopintern	logical	FALSE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
oobweights	untyped			-
baselearner	character	bbs	bbs, bols, btree	-
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 -> LearnerSurvMBoost

Methods

Public methods

• LearnerSurvMBoost$new()

• LearnerSurvMBoost$importance()

• LearnerSurvMBoost$selected_features()

• LearnerSurvMBoost$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

LearnerSurvMBoost$new()

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Method importance()

The importance scores are extracted with the function mboost::varimp() with the default arguments.

Usage

LearnerSurvMBoost$importance()

Returns

Named numeric().

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Method selected_features()

Selected features are extracted with the function mboost::variable.names.mboost(), with used.only = TRUE.

Usage

LearnerSurvMBoost$selected_features()

Returns

character().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("surv.mboost")
print(learner)
#> <LearnerSurvMBoost:surv.mboost>: Boosted Generalized Additive Model
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, mboost
#> * Predict Types:  crank, [distr], lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: importance, selected_features, weights

# available parameters:
learner$param_set$ids()
#>  [1] "family"        "custom.family" "nuirange"      "offset"       
#>  [5] "center"        "mstop"         "nu"            "risk"         
#>  [9] "stopintern"    "trace"         "oobweights"    "baselearner"  
#> [13] "sigma"         "ipcw"          "na.action"