On the Fairness capability of CatBoost

Providing extensive support for categorical data in a ready to use ap- proach, enhanced computational performance, overfitting reduction and fast predictions, CatBoost is the state-of-the-art model for the tabular datasets. However, in research about fairness LightGBM gained the spot- light as gradient boost decision trees based algorithm with the well-known implementation called FairGBM that ingrains a in-process schema to guar- antee fairness.
The proposed project aims to explore the effects of fairness approaches on CatBoost considering several datasets to assess gains and losses in terms of resulting fairness and overall performances.
During the last quinquennium the research community focused its efforts on a set of datasets called ACS (used to empirically validate FairGBM), the proposed project adds other data sources as well in order to better comprehend the potentiality of CatBoost when fairness is involved.