Calls CoxBoost::CoxBoost from package CoxBoost.

Details

Use LearnerSurvCoxboost and LearnerSurvCVCoxboost for Cox boosting without and with internal cross-validation of boosting step number, respectively. Tuning using the internal optimizer in LearnerSurvCVCoxboost may be more efficient when tuning stepno only. However, for tuning multiple hyperparameters, mlr3tuning and LearnerSurvCoxboost will likely give better results.

Dictionary

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

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

Traits

  • Packages: CoxBoost, pracma

  • Predict Types: distr, crank, lp

  • Feature Types: integer, numeric

  • Properties: weights

References

Binder, H., Allignol, A., Schumacher, M., and Beyersmann, J. (2009). Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics, 25:890-896.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxboost

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCoxboost$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.coxboost")) print(learner)
#> <LearnerSurvCoxboost:surv.coxboost> #> * Model: - #> * Parameters: list() #> * Packages: CoxBoost, pracma #> * Predict Type: distr #> * Feature types: integer, numeric #> * Properties: weights
# available parameters: learner$param_set$ids()
#> [1] "unpen.index" "standardize" "stepno" "penalty" #> [5] "criterion" "stepsize.factor" "sf.scheme" "pendistmat" #> [9] "connected.index" "x.is.01" "return.score" "trace" #> [13] "at.step"