Transcription of XGBoost: A Scalable Tree Boosting System
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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 Machine Learning1. INTRODUCTIONM achine learning and data-driven approaches are becom-ing very important in many areas. Smart spam classifiersprotect our email by learning from massive amounts of s-pam data and user feedback; advertising systems learn tomatch the right ads with the right context; fraud detectionsystems protect banks from malicious attackers; anomalyevent detection systems help experimental physicists to findevents that lead to new physics.
gradient tree boosting [10]1 is one technique that shines in many applications. Tree boosting has been shown to give state-of-the-art results on many standard classi cation benchmarks [16]. LambdaMART [5], a variant of tree boost-ing for ranking, achieves state-of-the-art result for ranking 1Gradient tree boosting is also known as gradient boosting
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