mlr_learners_regr.fairnclm {mlr3fairness} | R Documentation |
Regression Non-convex Fair Regression Learner
Description
Calls fairml::nclm from package fairml.
Details
Fair regression model based on nonconvex optimization from Komiyama et al. (2018).
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$get("regr.fairnclm") lrn("regr.fairnclm")
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Parameters
Id | Type | Default | Levels | Range |
lambda | numeric | 0 | [0, \infty) |
|
save.auxiliary | logical | FALSE | TRUE, FALSE | - |
covfun | untyped | "stats::cov" | - | |
unfairness | numeric | - | [0, 1] |
|
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrFairnclm
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
LearnerRegrFairnclm$new()
Method clone()
The objects of this class are cloneable with this method.
Usage
LearnerRegrFairnclm$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Author(s)
pfistfl
References
J K, A T, J H, H S (2018). “Nonconvex Optimization for Regression with Fairness Constraints.” In Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 2737-2746.
See Also
Dictionary of Learners: mlr3::mlr_learners
Other fairness_learners:
mlr_learners_classif.fairfgrrm
,
mlr_learners_classif.fairzlrm
,
mlr_learners_regr.fairfrrm
,
mlr_learners_regr.fairzlm
Examples
library("mlr3")
# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("regr.fairnclm"))
print(learner)
# available parameters:
learner$param_set$ids()