XGBoost: A Scalable Tree Boosting System

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Authors Tianqi Chen, Carlos Guestrin
Journal/Conference Name Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language Multiple
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