Linear Model Regression Learner

Ordinary linear regression. Calls stats::lm().

Dictionary

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

mlr_learners$get("regr.lm")
lrn("regr.lm")

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::LearnerRegr -> LearnerRegrLM

Methods

Public methods

• LearnerRegrLM$new()

• LearnerRegrLM$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()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrLM$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrLM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("stats")) {
  learner = mlr3::lrn("regr.lm")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerRegrLM:regr.lm>
#> * Model: -
#> * Parameters: list()
#> * Packages: stats
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, factor
#> * Properties: weights
#>  [1] "x"           "y"           "model"       "qr"          "singular.ok"
#>  [6] "offset"      "se.fit"      "scale"       "df"          "interval"   
#> [11] "level"       "pred.var"