# MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling

arXiv: https://arxiv.org/abs/2011.07218 · HTML: https://mlsanity.com/paper/2011.07218/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier](https://mlsanity.com/paper/2206.00074/) (2022)
- [iLOCO: Distribution-Free Inference for Feature Interactions](https://mlsanity.com/paper/2502.06661/) (2025)
- [Fast and Interpretable Consensus Clustering via Minipatch Learning](https://mlsanity.com/paper/2110.02388/) (2021)
- [Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles](https://mlsanity.com/paper/2110.12067/) (2021)
- [Fair MP-BOOST: Fair and Interpretable Minipatch Boosting](https://mlsanity.com/paper/2404.01521/) (2024)
- [Cluster LOCO: Feature Importance For Interpreting Clusters](https://mlsanity.com/paper/2606.14592/) (2026)

## Abstract (arXiv)

Boosting methods are among the best general-purpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting method that yields comparable accuracy to popular AdaBoost and gradient boosting methods, yet is faster computationally and whose solution is more interpretable. We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration. By sequentially learning on tiny subsets of the data, our approach is computationally faster than other classic boosting algorithms. Also as it progresses, MP-Boost adaptively learns a probability distribution on the features and instances that upweight the most important features and challenging instances, hence adaptively selecting the most relevant minipatches for learning. These learned probability distributions also aid in interpretation of our method. We empirically demonstrate the interpretability, comparative accuracy, and computational time of our approach on a variety of binary classification tasks.