Boosted Generalized Linear Classification Learner

Fit a generalized linear classification model using a boosting algorithm. Calls mboost::glmboost() from mboost.

Dictionary

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

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

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
offset	numeric	NULL		\((-\infty, \infty)\)
family	character	Binomial	Binomial, AdaExp, AUC, custom	-
custom.family	untyped	-		-
link	character	logit	logit, probit	-
type	character	adaboost	glm, adaboost	-
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		\((-\infty, \infty)\)
nu	numeric	0.1		\((-\infty, \infty)\)
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped			-
trace	logical	FALSE	TRUE, FALSE	-
stopintern	untyped	FALSE		-
na.action	untyped	:: , stats , na.omit		-
contrasts.arg	untyped	-		-

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::LearnerClassif -> LearnerClassifGLMBoost

Methods

Public methods

• LearnerClassifGLMBoost$new()

• LearnerClassifGLMBoost$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()

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Method new()

Create a LearnerClassifGLMBoost object.

Usage

LearnerClassifGLMBoost$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGLMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("classif.glmboost")
print(learner)
#> <LearnerClassifGLMBoost:classif.glmboost>: Boosted Generalized Linear Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, mboost
#> * Predict Type: response
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: twoclass, weights

# available parameters:
learner$param_set$ids()
#>  [1] "offset"        "family"        "custom.family" "link"         
#>  [5] "type"          "center"        "mstop"         "nu"           
#>  [9] "risk"          "oobweights"    "trace"         "stopintern"   
#> [13] "na.action"     "contrasts.arg"