mlr_learners_surv.mboost: Boosted Generalized Additive Survival Learner

In mlr3learners/mlr3learner-mboost: Learners from mboost package for mlr3

Description

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")

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.

References

mlr3learners.mboostbuhlmann_2003

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

if (requireNamespace("mboost")) {
  learner = mlr3::lrn("surv.mboost")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

mlr3learners/mlr3learner-mboost documentation built on Aug. 9, 2020, 10:48 p.m.