mlr_learners_surv.mboost.Rd
Calls mboost::mboost 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$get("surv.mboost") lrn("surv.mboost")
Packages: mboost
Predict Types: distr, crank, lp
Feature Types: integer, numeric, factor, logical
Properties: importance, selected_features, weights
Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324–339. doi: 10.1198/016214503000125
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvMBoost
new()
Creates a new instance of this R6 class.
LearnerSurvMBoost$new()
importance()
The importance scores are extracted with the function mboost::varimp()
with the
default arguments.
LearnerSurvMBoost$importance()
Named numeric()
.
selected_features()
Selected features are extracted with the function mboost::variable.names.mboost()
, with
used.only = TRUE
.
LearnerSurvMBoost$selected_features()
clone()
The objects of this class are cloneable with this method.
LearnerSurvMBoost$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.mboost")) print(learner)#> <LearnerSurvMBoost:surv.mboost> #> * Model: - #> * Parameters: family=coxph #> * Packages: mboost #> * Predict Type: distr #> * Feature types: integer, numeric, factor, logical #> * Properties: importance, selected_features, weights# available parameters: learner$param_set$ids()#> [1] "family" "custom.family" "nuirange" "offset" #> [5] "center" "mstop" "nu" "risk" #> [9] "stopintern" "trace" "oobweights" "baselearner" #> [13] "sigma" "ipcw" "na.action"