Calls mboost::glmboost from package 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")

Traits

  • Packages: mboost

  • Predict Types: response, prob

  • Feature Types: integer, numeric, factor, ordered

  • Properties: twoclass, weights

References

Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324–339. doi: 10.1198/016214503000125

See also

Author

be-marc

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGLMBoost

Methods

Public methods

Inherited methods

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

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("classif.glmboost")) print(learner)
#> <LearnerClassifGLMBoost:classif.glmboost> #> * Model: - #> * Parameters: list() #> * Packages: mboost #> * Predict Type: response #> * Feature types: integer, numeric, factor, ordered #> * Properties: twoclass, weights
# available parameters: learner$param_set$ids()
#> [1] "offset" "family" "link" "type" #> [5] "center" "mstop" "nu" "risk" #> [9] "oobweights" "trace" "stopintern" "na.action" #> [13] "contrasts.arg"