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")

Traits

  • Packages: mboost, pracma

  • Predict Types: distr, crank, lp

  • Feature Types: integer, numeric, factor

  • Properties: weights

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

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvBlackBoost

Methods

Public methods

Inherited methods

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

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.blackboost")) print(learner)
#> <LearnerSurvBlackBoost:surv.blackboost> #> * Model: - #> * Parameters: family=coxph #> * Packages: mboost, pracma #> * Predict Type: distr #> * Feature types: integer, numeric, factor #> * Properties: weights
# available parameters: learner$param_set$ids()
#> [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"