mlr_learners_classif.fairfgrrm {mlr3fairness} | R Documentation |
Classification Fair Generalized Ridge Regression Learner
Description
Calls fairml::fgrrm from package fairml.
Details
Fair generalized ridge regression model implemented via package fairml
.
The 'unfairness' parameter is set to 0.05 as a default.
Dictionary
This mlr3::Learner can be instantiated via the
dictionary mlr3::mlr_learners or with the associated
sugar function mlr3::lrn()
:
mlr_learners$get("classif.fairfgrrm") lrn("classif.fairfgrrm")
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Parameters
Id | Type | Default | Levels | Range |
lambda | numeric | 0 | [0, \infty) |
|
definition | character | sp-komiyama | sp-komiyama, eo-komiyama | - |
save.auxiliary | logical | FALSE | TRUE, FALSE | - |
unfairness | numeric | - | [0, 1] |
|
family | character | binomial | gaussian, binomial | - |
intersect | logical | TRUE | TRUE, FALSE | - |
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifFairfgrrm
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
LearnerClassifFairfgrrm$new()
Method clone()
The objects of this class are cloneable with this method.
Usage
LearnerClassifFairfgrrm$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Author(s)
pfistfl
References
Scutari M, Panero F, Proissl M (2021). “Achieving Fairness with a Simple Ridge Penalty.” arXiv preprint arXiv:2105.13817.
See Also
Dictionary of Learners: mlr3::mlr_learners
Other fairness_learners:
mlr_learners_classif.fairzlrm
,
mlr_learners_regr.fairfrrm
,
mlr_learners_regr.fairnclm
,
mlr_learners_regr.fairzlm
Examples
library("mlr3")
# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("classif.fairfgrrm"))
print(learner)
# available parameters:
learner$param_set$ids()