“XGBoost: A Scalable Tree Boosting System”, 2016-03-09 (; backlinks; similar):
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.
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