Boosted Generalized Additive Survival Learner

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

Traits

• Packages: mboost

• 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

Dictionary of Learners: mlr3::mlr_learners

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvMBoost

Methods

Public methods

• LearnerSurvMBoost$new()

• LearnerSurvMBoost$importance()

• LearnerSurvMBoost$selected_features()

• LearnerSurvMBoost$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvMBoost$new()

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Method importance()

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

Usage

LearnerSurvMBoost$importance()

Returns

Named numeric().

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Method selected_features()

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

Usage

LearnerSurvMBoost$selected_features()

Returns

character().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvMBoost$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.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"