# XGBoost: A Scalable Tree Boosting System

arXiv: https://arxiv.org/abs/1603.02754 · HTML: https://mlsanity.com/paper/1603.02754/

## Key facts

- Cited by 5,166 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2016.

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## Abstract (arXiv)

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.