mlr_learners_regr.fairfrrm: Regression Fair Ridge Regression Learner

In mlr3fairness: Fairness Auditing and Debiasing for 'mlr3'

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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.fairfrrm")
lrn("regr.fairfrrm")

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, fairml

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

• LearnerRegrFairfrrm$new()

• LearnerRegrFairfrrm$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

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()

mlr3fairness documentation built on May 31, 2023, 7:22 p.m.