mlr_learners_surv.coxboost.Rd
Calls CoxBoost::CoxBoost from package CoxBoost.
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.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.coxboost") lrn("surv.coxboost")
Packages: CoxBoost, pracma
Predict Types: distr, crank, lp
Feature Types: integer, numeric
Properties: weights
Binder, H., Allignol, A., Schumacher, M., and Beyersmann, J. (2009). Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics, 25:890-896.
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCoxboost
new()
Creates a new instance of this R6 class.
LearnerSurvCoxboost$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvCoxboost$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.coxboost")) print(learner)#> <LearnerSurvCoxboost:surv.coxboost> #> * Model: - #> * Parameters: list() #> * Packages: CoxBoost, pracma #> * Predict Type: distr #> * Feature types: integer, numeric #> * Properties: weights# available parameters: learner$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"