The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
XGBoost: A Scalable Tree Boosting System
Open accessAbstract
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.
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“The XGBoost classifier is also optimized for fast, parallel tree construction, and designed to be fault tolerant under the distributed setting [29].”
From Prediction of Employee Turnover in Organizations using Machine Learning Algorithms (2016) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Compared to other gradient boosted machines, it uses a more regularized-model formalization to control over-fitting, which gives it better performance.”
From Prediction of Employee Turnover in Organizations using Machine Learning Algorithms (2016) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although the same algorithm was implemented, each study used different risk predictors and datasets, which resulted in widely disparate AUC values of 0.91, 0.83, and 0.679–11.”
From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [8]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“For example, the XGBoost algorithm—a machine learning algorithm that uses a sequentially iterative process to learn and correct previous errors—was used in three unimodal studies8.”
From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [8]
In the citing paper
“The top 10 predictors accounted for 43.7% of overall model performance based on the gain metric in the Extreme Gradient Boosting algorithm (77), the top 25 predictors for 58.1%, the top 50 predictors for 70.3%, and the top 100 predictors for 81.4% of model performance.”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [77]
In the citing paper
“Chen et al. described an end-to-end tree boosting system named XGBoost [51].”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [51]
In the citing paper
“Some other approaches, such as Xgboost [51] and AdaBoost, can reduce bias as it learns from misclassification.However, these algorithms are subtle to outliers and noise.[178]”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [51]
In the citing paper
“XGBoost was chosen due to its efficiency and capability of processing large datasets, its ability to handle missing data and imbalanced datasets, and (relative to other ML algorithms) its interpretability.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [40]
In the citing paper
“These fingerprints are used together with a 1900-dimensioanl enzyme representation, called UniRep vector 29 , as the input for a gradient boosted decision tree model 34 to predict the K M value for an enzyme-substrate pair.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [34]
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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MLlib: Machine Learning in Apache SparkPreprint
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Practical Lessons from Predicting Clicks on Ads at Facebook
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Ridgeway, G. K. (2006) · Cited by 805
Space-efficient online computation of quantile summaries
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McRank: Learning to Rank Using Multiple Classification and Gradient Boosting
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Stochastic gradient boosted distributed decision trees
Ye, J., Chow, J.-H., Chen, J., & Zheng, Z. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646301 · Cited by 343
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Yahoo! Learning to Rank Challenge Overview
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Learning Nonlinear Functions Using Regularized Greedy Forest
Johnson, R., & Zhang, T. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.159 · Cited by 181
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Tyree, S., Weinberger, K. Q., Agrawal, K., & Paykin, J. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963461 · Cited by 169
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A Fast Algorithm for Approximate Quantiles in High Speed Data Streams
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Research works printed but not linked: 7
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- J. Bennett and S. Lanning. The netflix prize. In Proceedings of the KDD Cup Workshop 2007, pages 3--6, New York, Aug. 2007.
- T. Chen, H. Li, Q. Yang, and Y. Yu. General functional matrix factorization using gradient boosting. In Proceeding of 30th International Conference on Machine Learning (ICML'13), volume 1, pages 436--444, 2013.
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Cited by
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From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143
SoilGrids250m: Global gridded soil information based on machine learning
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733
Sagi, O., & Rokach, L. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1249 · Cited by 3,103
Metadata
- Authors
- Chen, T., & Guestrin, C.
- Affiliations
- University of Washington (US)
- ORCID
- Guestrin, Carlos
- Venue
- Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2016-08-13
- Pages
- 785-794
- OpenAlex
- W2295598076
- Cited by
- 54,569
- References
- 21
- Citations by year
- 2026: 12394 · 2025: 11769 · 2024: 8715 · 2023: 6731 · 2022: 5369 · 2021: 4238 · 2020: 2665 · 2019: 1560 · 2018: 779 · 2017: 294 · 2016: 48
- FWCI
- 936.68
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- Office of Naval Research (N000141010672)
- National Science Foundation (1258741)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- XGBoost, gradient boosting decision tree, gradient boosting, data sparsity, data compression, data sharding, scalability, large-scale machine learning
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