Package: fairml 0.9.1
Marco Scutari
fairml: Fair Models in Machine Learning
Fair machine learning regression models which take sensitive attributes into account in model estimation. Currently implementing Komiyama et al. (2018) <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>, Zafar et al. (2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own approach from Scutari, Panero and Proissl (2022) <doi:10.1007/s11222-022-10143-w> that uses ridge regression to enforce fairness.
Authors:
fairml_0.9.1.tar.gz
fairml_0.9.1.tar.gz(r-4.7-any)fairml_0.9.1.tar.gz(r-4.6-any)
fairml_0.9.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
fairml/json (API)
| # Install 'fairml' in R: |
| install.packages('fairml', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- adult - Census Income
- bank - Bank Marketing
- communities.and.crime - Communities and Crime Data Set
- compas - Criminal Offenders Screened in Florida
- drug.consumption - Drug Consumption
- german.credit - German Credit Data
- health.retirement - Health and Retirement Survey
- law.school.admissions - Law School Admission Council data
- national.longitudinal.survey - Income and Labour Market Activities
- obesity.levels - Obesity Levels
- vu.test - Synthetic Data Set to Test Fair Models
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:9f4156f920. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 504 | ||
| source / vignettes | OK | 162 | ||
| linux-release-x86_64 | OK | 153 | ||
| wasm-release | OK | 107 |
Exports:cv.foldscv.losscv.unfairnessfairml.cvfairness.profile.plotfgrrmfrrmnclmzlmzlm.origzlrmzlrm.orig
Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fair Models in Machine Learning | fairml-package fairml |
| Census Income | adult |
| Bank Marketing | bank |
| Communities and Crime Data Set | communities.and.crime |
| Criminal Offenders Screened in Florida | compas |
| Confidence Intervals for Fair Models | confint.fair.model plot.fair.confint |
| Drug Consumption | drug.consumption |
| Cross-Validation for Fair Models | cv.folds cv.loss cv.unfairness fairml.cv |
| Profile Fair Models with Respect to Tuning Parameters | fairness.profile.plot |
| Obesity Levels | flchain |
| Fair Ridge Regression Model | fgrrm frrm |
| German Credit Data | german.credit |
| Health and Retirement Survey | health.retirement |
| Law School Admission Council data | law.school.admissions |
| Extract Information from fair.model Objects | all.equal.fair.model coef.fair.model deviance.fair.model fitted.fair.model logLik.fair.model methods for fair.model objects nobs.fair.model plot.fair.model predict.fgrrm predict.frrm predict.nclm predict.zlm predict.zlrm print.fair.model residuals.fair.model sigma.fair.model summary.fair.model |
| Income and Labour Market Activities | national.longitudinal.survey |
| Nonconvex Optimization for Regression with Fairness Constraints | nclm |
| Obesity Levels | obesity.levels |
| Synthetic Data Set to Test Fair Models | vu.test |
| Zafar's Linear and Logistic Regressions | zlm zlm.orig zlrm zlrm.orig |