Web: http://arxiv.org/abs/2209.07850

Sept. 19, 2022, 1:11 a.m. | André F Cruz, Catarina Belém, João Bravo, Pedro Saleiro, Pedro Bizarro

cs.LG updates on arXiv.org arxiv.org

Machine Learning (ML) algorithms based on gradient boosted decision trees
(GBDT) are still favored on many tabular data tasks across various mission
critical applications, from healthcare to finance. However, GBDT algorithms are
not free of the risk of bias and discriminatory decision-making. Despite GBDT's
popularity and the rapid pace of research in fair ML, existing in-processing
fair ML methods are either inapplicable to GBDT, incur in significant train
time overhead, or are inadequate for problems with high class imbalance. We …

arxiv boosting constraints fairness gradient

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