# XGBoost: A Scalable Tree Boosting System

> ## Abstract

## 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](https://wiki.g15e.com/pages/XGBoost.txt), which is used widely by data scientists to achieve state-of-the-art results on many [machine learning](https://wiki.g15e.com/pages/Machine%20learning.txt) 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.

https://arxiv.org/abs/1603.02754