Package: fairmodels 1.2.1

Jakub Wiśniewski

fairmodels: Flexible Tool for Bias Detection, Visualization, and Mitigation

Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arxiv:2104.00507>.

Authors:Jakub Wiśniewski [aut, cre], Przemysław Biecek [aut]

fairmodels_1.2.1.tar.gz
fairmodels_1.2.1.zip(r-4.5)fairmodels_1.2.1.zip(r-4.4)fairmodels_1.2.1.zip(r-4.3)
fairmodels_1.2.1.tgz(r-4.4-any)fairmodels_1.2.1.tgz(r-4.3-any)
fairmodels_1.2.1.tar.gz(r-4.5-noble)fairmodels_1.2.1.tar.gz(r-4.4-noble)
fairmodels_1.2.1.tgz(r-4.4-emscripten)fairmodels_1.2.1.tgz(r-4.3-emscripten)
fairmodels.pdf |fairmodels.html✨
fairmodels/json (API)
NEWS

# Install 'fairmodels' in R:

install.packages('fairmodels', repos = c('https://modeloriented.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/modeloriented/fairmodels/issues

Datasets:

adult - Adult dataset
adult_test - Adult test dataset
compas - Modified COMPAS dataset
german - Modified German Credit data dataset

On CRAN:

explain-classifiers explainable-ml fairness fairness-comparison fairness-ml model-evaluation

7.59 score 86 stars 1 packages 50 scripts 384 downloads 25 exports 37 dependencies

Last updated 2 years agofrom:ed28759990. Checks:OK: 3 NOTE: 4. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 10 2024
R-4.5-win	NOTE	Nov 10 2024
R-4.5-linux	NOTE	Nov 10 2024
R-4.4-win	NOTE	Nov 10 2024
R-4.4-mac	NOTE	Nov 10 2024
R-4.3-win	OK	Nov 10 2024
R-4.3-mac	OK	Nov 10 2024

Exports:all_cutoffs calculate_group_fairness_metrics ceteris_paribus_cutoff choose_metric confusion_matrix disparate_impact_remover expand_fairness_object fairness_check fairness_check_regression fairness_heatmap fairness_pca fairness_radar group_matrices group_metric group_model_performance metric_scores performance_and_fairness plot_density plot_fairmodels pre_process_data regression_metrics resample reweight roc_pivot stack_metrics

Dependencies:cli codetools colorspace DALEX fansi farver foreach ggplot2 glue gridExtra gtable iBreakDown ingredients isoband iterators kernelshap labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme patchwork pillar pkgconfig R6 RColorBrewer rlang scales tibble utf8 vctrs viridisLite withr

Advanced Tutorial

Jakub Wiśniewski

Rendered fromAdvanced_tutorial.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2021-10-08
Started: 2020-07-21

Basic Tutorial

Jakub Wiśniewski

Rendered fromBasic_tutorial.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2021-10-08
Started: 2020-05-15

Help page	Topics
Adult dataset	adult
Adult test dataset	adult_test
All cutoffs	all_cutoffs
Calculate fairness metrics in groups	calculate_group_fairness_metrics
Ceteris paribus cutoff	ceteris_paribus_cutoff
Choose metric	choose_metric
Modified COMPAS dataset	compas
Confusion matrix	confusion_matrix
Disparate impact remover	disparate_impact_remover
Expand Fairness Object	expand_fairness_object
Fairness check	fairness_check
Fairness check regression	fairness_check_regression
Fairness heatmap	fairness_heatmap
Fairness PCA	fairness_pca
Fairness radar	fairness_radar
Modified German Credit data dataset	german
Group confusion matrices	group_matrices
Group metric	group_metric
Group model performance	group_model_performance
Metric scores	metric_scores
Performance and fairness	performance_and_fairness
Plot fairness object	plot_density
Plot fairmodels	plot_fairmodels plot_fairmodels.default plot_fairmodels.explainer plot_fairmodels.fairness_object
Plot all cutoffs	plot.all_cutoffs
Ceteris paribus cutoff plot	plot.ceteris_paribus_cutoff
Plot chosen metric	plot.chosen_metric
Plot Heatmap	plot.fairness_heatmap
Plot fairness object	plot.fairness_object
Plot fairness PCA	plot.fairness_pca
Plot fairness radar	plot.fairness_radar
Plot fairness regression object	plot.fairness_regression_object
Plot group metric	plot.group_metric
Plot metric scores	plot.metric_scores
Plot fairness and performance	plot.performance_and_fairness
Plot stacked Metrics	plot.stacked_metrics
Pre-process data	pre_process_data
Print all cutoffs	print.all_cutoffs
Print ceteris paribus cutoff	print.ceteris_paribus_cutoff
Print chosen metric	print.chosen_metric
Print fairness heatmap	print.fairness_heatmap
Print Fairness Object	print.fairness_object
Print fairness PCA	print.fairness_pca
Print fairness radar	print.fairness_radar
Print Fairness Regression Object	print.fairness_regression_object
Print group metric	print.group_metric
Print metric scores data	print.metric_scores
Print performance and fairness	print.performance_and_fairness
Print stacked metrics	print.stacked_metrics
Regression metrics	regression_metrics
Resample	resample
Reweight	reweight
Reject Option based Classification pivot	roc_pivot
Stack metrics	stack_metrics

Package: fairmodels 1.2.1

fairmodels: Flexible Tool for Bias Detection, Visualization, and Mitigation

Advanced Tutorial

Basic Tutorial

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)