XGBoost: A Scalable Tree Boosting System（XGBoost：一个可扩展的树提升系统）

Adam婷

于 2019-07-15 20:42:38 发布

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分类专栏： AI程序员算法机器学习数据科学 Python 程序员论文研读

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XGBoost是一个可扩展的端到端树增强系统，被广泛用于数据科学家，以在各种机器学习挑战中实现最先进的结果。它提供了一种处理稀疏数据的新算法和加权分位数草图，以实现近似树学习。系统设计考虑了缓存访问、数据压缩和分片，使其能够在资源有限的情况下扩展到数十亿个示例。

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XGBoost: A Scalable Tree Boosting System

ABSTRACT

Tree boosting is a highly e ective 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 syste