# XGBoost: A Scalable Tree Boosting System

**Authors:** Tianqi Chen, Carlos Guestrin  
**arXiv:** [1603.02754](https://arxiv.org/abs/1603.02754)  
**Date:** 2016-03-09  
**Summary page:** https://www.summarizepaper.com/fr/paper/1603.02754v1/

## Abstract

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.