Calls gbm::gbm from package gbm.

Dictionary

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

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

Traits

  • Packages: gbm

  • Predict Types: response, prob

  • Feature Types: integer, numeric, factor, ordered

  • Properties: importance, missings, multiclass, twoclass, weights

Custom mlr3 defaults

  • 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.

See also

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGBM

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifGBM$new()


Method importance()

The importance scores are extracted by gbm::relative.influence() from the model.

Usage

LearnerClassifGBM$importance()

Returns

Named numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGBM$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("classif.gbm")) print(learner)
#> <LearnerClassifGBM:classif.gbm> #> * Model: - #> * Parameters: keep.data=FALSE, n.cores=1 #> * Packages: gbm #> * Predict Type: response #> * Feature types: integer, numeric, factor, ordered #> * Properties: importance, missings, multiclass, twoclass, weights
# available parameters: learner$param_set$ids()
#> [1] "distribution" "n.trees" "interaction.depth" #> [4] "n.minobsinnode" "shrinkage" "bag.fraction" #> [7] "train.fraction" "cv.folds" "keep.data" #> [10] "verbose" "n.cores" "var.monotone"