XGBoost: A Scalable Tree Boosting System Tianqi Chen University of Washington tqchen@cs.washington.edu Carlos Guestrin University of Washington guestrin@cs.washington.edu 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 quan- tile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compres- sion 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. Keywords Large-scale Machine Learning 1. INTRODUCTION Machine learning and data-driven approaches are becom- ing very important in many are
