mlr_learners_surv.gbm: Survival Gradient Boosting Machine Learner

In mlr3learners/mlr3learners.gbm: gbm learner from gbm package for mlr3

Description

Regression gradient boosting machine models. Calls gbm::gbm() from package gbm.

Custom mlr3 defaults

• distribution:

• Actual default: "bernoulli"

• Adjusted default: "coxph"

• Reason for change: This is the only distribution available for survival.

• keep_data:

  • Actual default: TRUE

  • Adjusted default: FALSE

  • Reason for change: keep_data = FALSE saves memory during model fitting.

• n.cores:

  • Actual default: NULL

  • Adjusted default: 1

  • Reason for change: Suppressing the automatic internal parallelization if cv.folds > 0.

Dictionary

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

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

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGBM

Methods

Public methods

• LearnerSurvGBM$new()

• LearnerSurvGBM$importance()

• LearnerSurvGBM$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvGBM$new()

---

Method importance()

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerSurvGBM$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

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

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

mlr3learners/mlr3learners.gbm documentation built on Sept. 4, 2020, 6:57 a.m.