Description Details Dictionary Super classes Methods References See Also Examples
A mlr3proba::LearnerSurv implementing coxboost from package
CoxBoost.
Calls CoxBoost::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()
:
1 2 | mlr_learners$get("surv.coxboost")
lrn("surv.coxboost")
|
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCoxboost
new()
Creates a new instance of this R6 class.
LearnerSurvCoxboost$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvCoxboost$clone(deep = FALSE)
deep
Whether to make a deep clone.
Binder, H., Allignol, A., Schumacher, M., and Beyersmann, J. (2009). Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics, 25:890-896.
Dictionary of Learners: mlr3::mlr_learners
1 2 3 4 5 6 7 | if (requireNamespace("CoxBoost")) {
learner = mlr3::lrn("surv.coxboost")
print(learner)
# available parameters:
learner$param_set$ids()
}
|
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