Generalized Linear Regression
mlr_learners_regr.glm.Rd
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 the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("regr.glm")
mlr_learnerslrn("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 | - | ||
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 | - | ||
type | character | link | response, link, terms | - |
Custom mlr3 defaults
type
Actual default: "link"
Adjusted default: "response"
Reason for change: Response scale more natural for predictions.
References
Hosmer Jr, W D, Lemeshow, Stanley, Sturdivant, X R (2013). Applied logistic regression, volume 398. John Wiley & Sons.
See also
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.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrGlm
Examples
learner = mlr3::lrn("regr.glm")
print(learner)
#> <LearnerRegrGlm:regr.glm>: Generalized Linear Regression
#> * Model: -
#> * Parameters: family=gaussian, type=response
#> * Packages: mlr3, mlr3extralearners, stats
#> * Predict Types: [response], se
#> * Feature Types: logical, integer, numeric, character, factor, ordered
#> * Properties: weights
# available parameters:
learner$param_set$ids()
#> [1] "singular.ok" "x" "y" "model" "etastart"
#> [6] "mustart" "start" "offset" "family" "na.action"
#> [11] "link" "epsilon" "maxit" "trace" "dispersion"
#> [16] "type"