XGBoost: A Scalable Tree Boosting System

xgboost : A Scalable tree Boosting SystemTianqi ChenUniversity of GuestrinUniversity of Boosting is a highly effective and widely used machinelearning method. In this paper, we describe a Scalable end-to-end tree Boosting System called xgboost , which is usedwidely by data scientists to achieve state-of-the-art resultson many machine learning challenges. We propose a novelsparsity-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 combining these insights, xgboost scales beyond billionsof examples using far fewer resources than existing Concepts Methodologies Machine learning; Informationsystems data mining;KeywordsLarge-scale Machine Learning1. INTRODUCTIONM achine learning and data -driven approaches are becom-ing very important in many areas.

While domain depen-dent data analysis and feature engineering play an important role in these solutions, the fact that XGBoost is the consen- ... While there are some existing works on parallel tree boost-ing [19,20,16], the directions such as out-of-core compu-tation, cache-aware and sparsity-aware learning have not

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  System, Data, Tree, Boost, Boosting, Domain, Scalable, Xgboost, A scalable tree boosting system

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