Abstract
Tree boosting is a highly effective and widely used machine learning method.\nIn this paper, we describe a scalable end-to-end tree boosting system called\nXGBoost, which is used widely by data scientists to achieve state-of-the-art\nresults on many machine learning challenges. We propose a novel sparsity-aware\nalgorithm for sparse data and weighted quantile sketch for approximate tree\nlearning. More importantly, we provide insights on cache access patterns, data\ncompression and sharding to build a scalable tree boosting system. By combining\nthese insights, XGBoost scales beyond billions of examples using far fewer\nresources than existing systems.\n
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- Year
- 2016
- Type
- article
- Pages
- 785-794
- Citations
- 41264
- Access
- Closed
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Cite This
Tianqi Chen,
Carlos Guestrin
(2016).
XGBoost.
, 785-794.
https://doi.org/10.1145/2939672.2939785
Identifiers
- DOI
- 10.1145/2939672.2939785