mlr_learners_regr.glm: Generalized Linear Regression
| mlr_learners_regr.glm | R Documentation |
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 | - | |
| 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 | - |
| use_pred_offset | logical | TRUE | TRUE, FALSE | - |
Initial parameter values
-
typeActual default:
"link"Adjusted default:
"response"Reason for change: Response scale more natural for predictions.
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::glm().
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.
Super classes
mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGlm
Methods
Public methods
Inherited methods
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
deepWhether 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()
