Logistic Regression Classification Learner

Classification via logistic regression. Calls stats::glm() with family set to "binomial". Argument model is set to FALSE.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.log_reg")
lrn("classif.log_reg")

Contrasts

To ensure reproducibility, this learner always uses the default contrasts:

• contr.treatment() for unordered factors, and

• contr.poly() for ordered factors.

Setting the option "contrasts" does not have any effect. Instead, set the respective hyperparameter or use mlr3pipelines to create dummy features.

See also

Dictionary of Learners: mlr3::mlr_learners

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLogReg

Methods

Public methods

• LearnerClassifLogReg$new()

• LearnerClassifLogReg$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

LearnerClassifLogReg$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLogReg$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("stats")) {
  learner = mlr3::lrn("classif.log_reg")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifLogReg:classif.log_reg>
#> * Model: -
#> * Parameters: list()
#> * Packages: stats
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, character, factor, ordered
#> * Properties: twoclass, weights
#>  [1] "singular.ok" "x"           "y"           "model"       "etastart"   
#>  [6] "mustart"     "start"       "offset"      "epsilon"     "maxit"      
#> [11] "trace"       "se.fit"      "dispersion"