mlr_learners_regr.fairfrrm {mlr3fairness} | R Documentation |
Regression Fair Ridge Regression Learner
Description
If more than one pta
columns are provided, the hyperparameter intersectional
controls whether
intersections of protected groups are formed (e.g. combinations of gender and race).
Initialized to TRUE
.
If FALSE
, only the group specified by the first element of pta
is used.
Calls fairml::frrm from package fairml.
Details
Fair ridge regression learner implemented via package fairml
.
The 'unfairness' parameter has been initialized to 0.05.
Dictionary
This mlr3::Learner can be instantiated via the
dictionary mlr3::mlr_learners or with the associated
sugar function mlr3::lrn()
:
mlr_learners$get("regr.fairfrrm") lrn("regr.fairfrrm")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Parameters
Id | Type | Default | Levels | Range |
lambda | numeric | 0 | [0, \infty) |
|
definition | character | sp-komiyama | sp-komiyama, eo-komiyama | - |
save.auxiliary | logical | FALSE | TRUE, FALSE | - |
unfairness | numeric | - | [0, 1] |
|
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrFairfrrm
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
LearnerRegrFairfrrm$new()
Method clone()
The objects of this class are cloneable with this method.
Usage
LearnerRegrFairfrrm$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Author(s)
pfistfl
References
Scutari M, Panero F, Proissl M (2021). “Achieving Fairness with a Simple Ridge Penalty.” arXiv preprint arXiv:2105.13817.
See Also
Dictionary of Learners: mlr3::mlr_learners
Other fairness_learners:
mlr_learners_classif.fairfgrrm
,
mlr_learners_classif.fairzlrm
,
mlr_learners_regr.fairnclm
,
mlr_learners_regr.fairzlm
Examples
library("mlr3")
# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("regr.fairfrrm"))
print(learner)
# available parameters:
learner$param_set$ids()