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")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3extralearners, gbm

Parameters

Id	Type	Default	Levels	Range
distribution	character	gaussian	gaussian, laplace, poisson, tdist, quantile	\((-\infty, \infty)\)
n.trees	integer	100		\([1, \infty)\)
interaction.depth	integer	1		\([1, \infty)\)
n.minobsinnode	integer	10		\([1, \infty)\)
shrinkage	numeric	0.001		\([0, \infty)\)
bag.fraction	numeric	0.5		\([0, 1]\)
train.fraction	numeric	1		\([0, 1]\)
cv.folds	integer	0		\((-\infty, \infty)\)
alpha	numeric	0.5		\([0, 1]\)
keep.data	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
verbose	logical	FALSE	TRUE, FALSE	\((-\infty, \infty)\)
n.cores	integer	1		\((-\infty, \infty)\)
var.monotone	list	-		\((-\infty, \infty)\)

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.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

be-marc

Super classes

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

Methods

Public methods

• LearnerRegrGBM$new()

• LearnerRegrGBM$importance()

• LearnerRegrGBM$clone()

Inherited methods

• mlr3::Learner$base_learner()

• 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

LearnerRegrGBM$new()

---

Method importance()

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

Usage

LearnerRegrGBM$importance()

Returns

Named numeric().

---

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

if (requireNamespace("gbm", quietly = TRUE)) {
  learner = mlr3::lrn("regr.gbm")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerRegrGBM:regr.gbm>
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3extralearners, gbm
#> * Predict Type: response
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weights
#>  [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"