【文件属性】：
文件名称：陈天奇xgb论文《XGBoost: A Scalable Tree Boosting System》
文件大小：922KB
文件格式：PDF
更新时间：2021-10-10 07:50:45
XGBoost 机器学习 论文
陈天奇xgb论文。Tree boosting is a highly eective and widely used machine learning method. In this paper, we describe a scalable endto-
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.