Web: http://arxiv.org/abs/2201.09932

Jan. 26, 2022, 2:10 a.m. | Nathanael Jo, Sina Aghaei, Jack Benson, Andrés Gómez, Phebe Vayanos

cs.LG updates on arXiv.org arxiv.org

The increasing use of machine learning in high-stakes domains -- where
people's livelihoods are impacted -- creates an urgent need for interpretable
and fair algorithms. In these settings it is also critical for such algorithms
to be accurate. With these needs in mind, we propose a mixed integer
optimization (MIO) framework for learning optimal classification trees of fixed
depth that can be conveniently augmented with arbitrary domain specific
fairness constraints. We benchmark our method against the state-of-the-art
approach for building …

arxiv classification learning

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