%0 Generic %1 chen2016xgboost %A Chen, Tianqi %A Guestrin, Carlos %D 2016 %K ai boost tree xgboost %R 10.1145/2939672.2939785 %T XGBoost: A Scalable Tree Boosting System %U http://arxiv.org/abs/1603.02754 %X 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.

@misc{chen2016xgboost, 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.}, added-at = {2021-02-13T13:49:01.000+0100}, author = {Chen, Tianqi and Guestrin, Carlos}, biburl = {https://www.bibsonomy.org/bibtex/252bab0edcc868d4c831b85859ad7f0c1/louissf}, description = {XGBoost: A Scalable Tree Boosting System}, doi = {10.1145/2939672.2939785}, interhash = {f686ba00eda9a53bd2175fbe3f242c71}, intrahash = {52bab0edcc868d4c831b85859ad7f0c1}, keywords = {ai boost tree xgboost}, note = {cite arxiv:1603.02754Comment: KDD'16 changed all figures to type1}, timestamp = {2021-02-13T13:49:01.000+0100}, title = {XGBoost: A Scalable Tree Boosting System}, url = {http://arxiv.org/abs/1603.02754}, year = 2016 }