Linear Model Regression Learner

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

Offset

If a Task has a column with the role offset, it will automatically be used during training. The offset is incorporated through the formula interface to ensure compatibility with stats::lm(). We add it to the model formula as offset(<column_name>) and also include it in the training data. During prediction, the default behavior is to use the offset column from the test set (enabled by use_pred_offset = TRUE). Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied, effectively disabling the offset adjustment during prediction.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

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

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

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

• 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	-
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	-
rankdeficient	character	-	warnif, simple, non-estim, NA, NAwarn	-
tol	numeric	1e-07		\((-\infty, \infty)\)
verbose	logical	FALSE	TRUE, FALSE	-
use_pred_offset	logical	TRUE	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.

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::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.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost

Super classes

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

Methods

Public methods

• LearnerRegrLM$new()

• LearnerRegrLM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrLM$new()

---

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", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.lm")
print(learner)

# Define a Task
task = tsk("mtcars")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# importance method
if("importance" %in% learner$properties) print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
}
#> <LearnerRegrLM:regr.lm>: Linear Model
#> * Model: -
#> * Parameters: use_pred_offset=TRUE
#> * Packages: mlr3, mlr3learners, stats
#> * Predict Types:  [response], se
#> * Feature Types: logical, integer, numeric, character, factor
#> * Properties: offset, weights
#> 
#> Call:
#> stats::lm(formula = form, data = data)
#> 
#> Coefficients:
#> (Intercept)           am         carb          cyl         disp         drat  
#>    16.30218      3.76393      0.13314     -0.81045      0.02792      0.90348  
#>        gear           hp         qsec           vs           wt  
#>     0.14509     -0.03059      0.80309      0.36197     -4.11977  
#> 
#> regr.mse 
#> 4.351582