Regression Generalized Linear Model Regression Learner

Calls stats::glm from package stats.

Details

For logistic regression please use mlr_learners_classif.log_reg.

Dictionary

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")

Traits

• Packages: stats

• Predict Types: response, se

• Feature Types: logical, integer, numeric, character, factor, ordered

• Properties: weights

Custom mlr3 defaults

• type

  • Actual default: "link"

  • Adjusted default: "response"

  • Reason for change: Response scale more natural for predictions.

See also

Dictionary of Learners: mlr3::mlr_learners

Author

salauer

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGlm

Methods

Public methods

• LearnerRegrGlm$new()

• LearnerRegrGlm$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrGlm$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGlm$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("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"