mlr_learners_regr.gamboost.Rd
Calls mboost::gamboost from package mboost.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("regr.gamboost") lrn("regr.gamboost")
Packages: mboost
Predict Types: response
Feature Types: integer, numeric, factor, ordered
Properties: weights
Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324–339. doi: 10.1198/016214503000125
be-marc
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrGAMBoost
new()
Create a LearnerRegrGAMBoost
object.
LearnerRegrGAMBoost$new()
clone()
The objects of this class are cloneable with this method.
LearnerRegrGAMBoost$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.gamboost")) print(learner)#> <LearnerRegrGAMBoost:regr.gamboost> #> * Model: - #> * Parameters: list() #> * Packages: mboost #> * Predict Type: response #> * Feature types: integer, numeric, factor, ordered #> * Properties: weights# available parameters: learner$param_set$ids()#> [1] "baselearner" "dfbase" "offset" "family" "nuirange" #> [6] "d" "mstop" "nu" "risk" "oobweights" #> [11] "trace" "stopintern" "na.action"