mlr_learners_regr.glm.Rd
Calls stats::glm from package stats.
For logistic regression please use
mlr_learners_classif.log_reg
.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("regr.glm") lrn("regr.glm")
Packages: stats
Predict Types: response, se
Feature Types: logical, integer, numeric, character, factor, ordered
Properties: weights
type
Actual default: "link"
Adjusted default: "response"
Reason for change: Response scale more natural for predictions.
salauer
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrGlm
new()
Creates a new instance of this R6 class.
LearnerRegrGlm$new()
clone()
The objects of this class are cloneable with this method.
LearnerRegrGlm$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.glm")) print(learner)#> <LearnerRegrGlm:regr.glm> #> * Model: - #> * Parameters: family=gaussian, type=response #> * Packages: stats #> * Predict Type: response #> * Feature types: logical, integer, numeric, character, factor, ordered #> * Properties: weights# available parameters: learner$param_set$ids()#> [1] "singular.ok" "x" "y" "model" "etastart" #> [6] "mustart" "start" "offset" "family" "na.action" #> [11] "link" "epsilon" "maxit" "trace" "dispersion" #> [16] "type"