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

Internal Encoding

Starting with mlr3 v0.5.0, the order of class labels is reversed prior to model fitting to comply to the stats::glm() convention that the negative class is provided as the first factor level.

Weights

It is not advisable to change the weights of a logistic regression. For more details, see this question on Cross Validated.

Custom mlr3 defaults

  • model:

    • Actual default: TRUE.

    • Adjusted default: FALSE.

    • Reason for change: Save some memory.

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")

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

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

  • Required Packages: mlr3, mlr3learners, 'stats'

Parameters

IdTypeDefaultLevelsRange
dispersionuntyped-
epsilonnumeric1e-08\((-\infty, \infty)\)
etastartuntyped--
maxitnumeric25\((-\infty, \infty)\)
modellogicalTRUETRUE, FALSE-
mustartuntyped--
offsetuntyped--
singular.oklogicalTRUETRUE, FALSE-
startuntyped-
tracelogicalFALSETRUE, FALSE-
xlogicalFALSETRUE, FALSE-
ylogicalTRUETRUE, FALSE-

Contrasts

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

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

See also

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method loglik()

Extract the log-likelihood (e.g., via stats::logLik() from the fitted model.

Usage

LearnerClassifLogReg$loglik()


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", quietly = TRUE)) {
  learner = mlr3::lrn("classif.log_reg")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifLogReg:classif.log_reg>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, stats
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: loglik, twoclass
#>  [1] "dispersion"  "epsilon"     "etastart"    "maxit"       "model"      
#>  [6] "mustart"     "offset"      "singular.ok" "start"       "trace"      
#> [11] "x"           "y"