mlr_learners_surv.blackboost.Rd
Calls mboost::blackboost from package mboost.
distr
prediction made by mboost::survFit()
.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.blackboost") lrn("surv.blackboost")
Packages: mboost, pracma
Predict Types: distr, crank, lp
Feature Types: integer, numeric, factor
Properties: weights
Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324–339. doi: 10.1198/016214503000125
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvBlackBoost
new()
Creates a new instance of this R6 class.
LearnerSurvBlackBoost$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvBlackBoost$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.blackboost")) print(learner)#> <LearnerSurvBlackBoost:surv.blackboost> #> * Model: - #> * Parameters: family=coxph #> * Packages: mboost, pracma #> * Predict Type: distr #> * Feature types: integer, numeric, factor #> * Properties: weights# available parameters: learner$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"