Boosted Generalized Linear Regression Learner
mlr_learners_regr.glmboost.Rd
Fit a generalized linear regression 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()
:
$get("regr.glmboost")
mlr_learnerslrn("regr.glmboost")
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 |
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 | \((-\infty, \infty)\) | |
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
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.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrGLMBoost
Examples
learner = mlr3::lrn("regr.glmboost")
print(learner)
#> <LearnerRegrGLMBoost:regr.glmboost>: Boosted Generalized Linear Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, mboost
#> * Predict Types: [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: weights
# available parameters:
learner$param_set$ids()
#> [1] "offset" "family" "custom.family" "nuirange"
#> [5] "d" "center" "mstop" "nu"
#> [9] "risk" "oobweights" "trace" "stopintern"
#> [13] "na.action" "contrasts.arg"