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"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."

Authors: Tianqi ChenCarlos Guestrin

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Tags: data, decision, learning, machine, science, trees


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Comment by Cameron Turner on March 17, 2016 at 9:04am

Cran R package inbound? :) 


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