Fair MP-BOOST: Fair and Interpretable Minipatch Boosting

arXiv:2404.01521v1 Announce Type: cross
Abstract: Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data. In this paper, we aim to leverage the robust predictive power of traditional boosting methods while enhancing fairness and interpretability. To achieve this, we develop Fair MP-Boost, a stochastic boosting scheme that balances fairness and accuracy by adaptively learning features and observations during training. Specifically, Fair MP-Boost sequentially samples small subsets of observations and features, termed …

abstract aim arxiv boost boosting cs.lg data ensemble fair fairness interpretability machine machine learning machine learning techniques paper power predictive robust stat.ml stochastic tabular tabular data type