Boosted Generalized Additive Regression Learner

Fit a generalized additive regression model using a boosting algorithm. Calls mboost::gamboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.gamboost")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
baselearner	character	bbs	bbs, bols, btree	-
dfbase	integer	4		\((-\infty, \infty)\)
offset	numeric	NULL		\((-\infty, \infty)\)
family	character	Gaussian	Gaussian, Laplace, Huber, Poisson, GammaReg, NBinomial, Hurdle, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
d	numeric	NULL		\((-\infty, \infty)\)
mstop	integer	100		\((-\infty, \infty)\)
nu	numeric	0.1		\((-\infty, \infty)\)
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped	NULL		-
trace	logical	FALSE	TRUE, FALSE	-
stopintern	untyped	FALSE		-
na.action	untyped	stats::na.omit		-

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

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 -> LearnerRegrGAMBoost

Methods

Public methods

• LearnerRegrGAMBoost$new()

• LearnerRegrGAMBoost$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Create a LearnerRegrGAMBoost object.

Usage

LearnerRegrGAMBoost$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGAMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("regr.gamboost", baselearner = "bols")
print(learner)
#> <LearnerRegrGAMBoost:regr.gamboost>: Boosted Generalized Additive Model
#> * Model: -
#> * Parameters: baselearner=bols
#> * Packages: mlr3, mlr3extralearners, mboost
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: weights