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Gradient Boosting for Survival Analysis. Calls gbm::gbm() from gbm.

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")

Meta Information

  • Task type: “surv”

  • Predict Types: “crank”, “lp”

  • Feature Types: “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, mlr3proba, mlr3extralearners, gbm

Parameters

IdTypeDefaultLevelsRange
distributioncharactercoxphcoxph-
n.treesinteger100\([1, \infty)\)
cv.foldsinteger0\([0, \infty)\)
interaction.depthinteger1\([1, \infty)\)
n.minobsinnodeinteger10\([1, \infty)\)
shrinkagenumeric0.001\([0, \infty)\)
bag.fractionnumeric0.5\([0, 1]\)
train.fractionnumeric1\([0, 1]\)
keep.datalogicalTRUETRUE, FALSE-
verboselogicalFALSETRUE, FALSE-
var.monotoneuntyped--
n.coresinteger1\((-\infty, \infty)\)
single.treelogicalFALSETRUE, FALSE-

Parameter changes

  • 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.

References

Friedman, H J (2002). “Stochastic gradient boosting.” Computational statistics & data analysis, 38(4), 367--378.

See also

Author

RaphaelS1

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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.

Examples

learner = mlr3::lrn("surv.gbm")
print(learner)
#> <LearnerSurvGBM:surv.gbm>: Gradient Boosting
#> * Model: -
#> * Parameters: distribution=coxph, keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3proba, mlr3extralearners, gbm
#> * Predict Type: crank
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weights

# available parameters:
learner$param_set$ids()
#>  [1] "distribution"      "n.trees"           "cv.folds"         
#>  [4] "interaction.depth" "n.minobsinnode"    "shrinkage"        
#>  [7] "bag.fraction"      "train.fraction"    "keep.data"        
#> [10] "verbose"           "var.monotone"      "n.cores"          
#> [13] "single.tree"