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FairRegBoost: An End-to-End Data Processing Framework for Fair and Scalable Regression

This repository contains the codes needed to reproduce the experiments of our submitted CIKM 2025 Paper

General Information

  • For the experiments we reuse work from other GitHubs (put in subfolders of algorithms)
  • For some approaches, some slight adaptations had to be made to integrate them in our framework, but the general approach was not altered.
  • The experiments were run under MacOS Sonoma 14.4, Python Version 3.9.6.

HOWTO RUN

  • You need to run run_exps_reg.py. The parameters to set for the experiments, like datasets, models to train, are in that file.
  • This will call the main.py function that runs the experiments, and subsequently calls evaluation_reg.py to evaluate the overall results.
  • An evaluation file is automatically generated for an experiment, which shows metrics for RMSE and W2, along additional metrics.

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