Boosted Generalized Linear Classification Learner

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

Dictionary of Learners: mlr3::mlr_learners

Author

be-marc

Super classes

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

Methods

Public methods

• LearnerClassifGLMBoost$new()

• LearnerClassifGLMBoost$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()

Create a LearnerClassifGLMBoost object.

Usage

LearnerClassifGLMBoost$new()

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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"        "custom.family" "link"         
#>  [5] "type"          "center"        "mstop"         "nu"           
#>  [9] "risk"          "oobweights"    "trace"         "stopintern"   
#> [13] "na.action"     "contrasts.arg"