陈天奇xgb论文《XGBoost: A Scalable Tree Boosting System》

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 sys tem. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems. tem. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.