Calls mboost::gamboost from package mboost.

Calls mboost::gamboost 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.gamboost")
lrn("surv.gamboost")

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("surv.gamboost")
lrn("surv.gamboost")

Traits

  • Packages: mboost, pracma

  • Predict Types: distr, crank, lp

  • Feature Types: integer, numeric, factor, logical

  • Properties: importance, selected_features, weights

  • Packages: mboost, pracma

  • Predict Types: distr, crank, lp

  • Feature Types: integer, numeric, factor, logical

  • Properties: importance, selected_features, 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 -> LearnerSurvGAMBoost

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvGAMBoost$new()


Method importance()

The importance scores are extracted with the function mboost::varimp() with the default arguments.

Usage

LearnerSurvGAMBoost$importance()

Returns

Named numeric().


Method selected_features()

Selected features are extracted with the function mboost::variable.names.mboost(), with used.only = TRUE.

Usage

LearnerSurvGAMBoost$selected_features()

Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGAMBoost$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.gamboost")) print(learner)
#> <LearnerSurvGAMBoost:surv.gamboost> #> * Model: - #> * Parameters: family=coxph #> * Packages: mboost, pracma #> * 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] "dfbase" "sigma" "ipcw" "na.action"