mlr3learners (version 0.5.3)

mlr_learners_regr.lm: Linear Model Regression Learner

Description

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")

Meta Information

, * Task type: “regr”, * Predict Types: “response”, “se”, * Feature Types: “logical”, “integer”, “numeric”, “factor”, “character”, * Required Packages: mlr3, mlr3learners, 'stats'

Parameters

, |Id |Type |Default |Levels |Range |, |:-----------|:---------|:-------|:----------------------------|:------------------------------------|, |df |numeric |Inf | |\((-\infty, \infty)\) |, |interval |character |- |none, confidence, prediction |- |, |level |numeric |0.95 | |\((-\infty, \infty)\) |, |model |logical |TRUE |TRUE, FALSE |- |, |offset |logical |- |TRUE, FALSE |- |, |pred.var |untyped |- | |- |, |qr |logical |TRUE |TRUE, FALSE |- |, |scale |numeric |NULL | |\((-\infty, \infty)\) |, |singular.ok |logical |TRUE |TRUE, FALSE |- |, |x |logical |FALSE |TRUE, FALSE |- |, |y |logical |FALSE |TRUE, FALSE |- |

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.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLM

Methods

Public methods

• LearnerRegrLM$new()

• LearnerRegrLM$loglik()

• LearnerRegrLM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$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 loglik()

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

Usage

LearnerRegrLM$loglik()

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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.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, 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.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Examples

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

  # available parameters:
learner$param_set$ids()
}