Regression Gradient Boosting Machine Learner

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("regr.gbm")
lrn("regr.gbm")

Traits

• Packages: gbm

• Predict Types: response

• Feature Types: integer, numeric, factor, ordered

• Properties: importance, missings, 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

Dictionary of Learners: mlr3::mlr_learners

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGBM

Methods

Public methods

• LearnerRegrGBM$new()

• LearnerRegrGBM$importance()

• LearnerRegrGBM$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

LearnerRegrGBM$new()

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Method importance()

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

Usage

LearnerRegrGBM$importance()

Returns

Named numeric().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGBM$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("regr.gbm"))
print(learner)
#> <LearnerRegrGBM:regr.gbm>
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: gbm
#> * Predict Type: response
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weights

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