mlr_learners_regr.glm: Generalized Linear Regression

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Generalized Linear Regression

Description

Generalized linear model. Calls stats::glm() from base package 'stats'. For logistic regression please use mlr_learners_classif.log_reg.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.glm")

Meta Information

• Task type: “regr”

• Predict Types: “response”, “se”

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

• Required Packages: mlr3, mlr3extralearners, 'stats'

Parameters

Id	Type	Default	Levels	Range
singular.ok	logical	TRUE	TRUE, FALSE	-
x	logical	FALSE	TRUE, FALSE	-
y	logical	TRUE	TRUE, FALSE	-
model	logical	TRUE	TRUE, FALSE	-
etastart	untyped	-		-
mustart	untyped	-		-
start	untyped	NULL		-
offset	untyped	-		-
family	character	gaussian	gaussian, poisson, quasipoisson, Gamma, inverse.gaussian	-
na.action	character	-	na.omit, na.pass, na.fail, na.exclude	-
link	character	-	logit, probit, cauchit, cloglog, identity, log, sqrt, 1/mu^2, inverse	-
epsilon	numeric	1e-08		(-\infty, \infty)
maxit	numeric	25		(-\infty, \infty)
trace	logical	FALSE	TRUE, FALSE	-
dispersion	untyped	NULL		-
type	character	link	response, link, terms	-

Initial parameter values

• type

  • Actual default: "link"

  • Adjusted default: "response"

  • Reason for change: Response scale more natural for predictions.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGlm

Methods

Public methods

• LearnerRegrGlm$new()

• LearnerRegrGlm$clone()

Inherited methods

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

LearnerRegrGlm$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrGlm$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

salauer

References

Hosmer Jr, W D, Lemeshow, Stanley, Sturdivant, X R (2013). Applied logistic regression, volume 398. John Wiley & Sons.

See Also

• 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).

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

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

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

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

Examples

# Define the Learner
learner = mlr3::lrn("regr.glm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

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

print(learner$model)


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

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.