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.
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“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.
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“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]
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“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]
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“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
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Scikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
LIBLINEAR: A Library for Large Linear Classification
Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,684
MLlib: Machine Learning in Apache SparkPreprint
Meng, X., Bradley, J. K., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S., et al. (2015) · Journal of machine learning research · doi:10.48550/arxiv.1505.06807 · Cited by 1,898
From RankNet to LambdaRank to LambdaMART: An Overview
Burges, C. J. C. (2010) · Cited by 1,011
Practical Lessons from Predicting Clicks on Ads at Facebook
He, X., Pan, J. J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., Bowers, S., et al. (2014) · doi:10.1145/2648584.2648589 · Cited by 892
Generalized Boosted Models: A guide to the gbm package
Ridgeway, G. K. (2006) · Cited by 805
Research works printed but not linked: 7
- 10.5555/2124408 · DOI
- 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.
- T. Chen, S. Singh, B. Taskar, and C. Guestrin. Efficient second-order gradient boosting for conditional random fields. In Proceeding of 18th Artificial Intelligence and Statistics Conference (AISTATS'15), volume 1, 2015.
- 10.5555/3023549.3023585 · DOI
- 10.5555/2981562.2981675 · DOI
- 10.5555/2946645.2946679 · DOI
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Wang, Y., & Wang, T. (2020) · Applied Sciences · doi:10.3390/app10093227 · Cited by 129
Evolution of Operations Management Research: from Managing Flows to Building Capabilities
Zhang, F., Wu, X., Tang, C. S., Feng, T., & Dai, Y. (2020) · Production and Operations Management · doi:10.1111/poms.13231 · Cited by 119
Liu, J., Kantarcı, B., & Adams, C. M. (2020) · ACM Workshop on Wireless Security and Machine Learning (WiseML) · doi:10.1145/3395352.3402621 · Cited by 119
Gan, M., Pan, S., Chen, Y., Chen, C., Pan, H., & Zhu, X. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9050496 · Cited by 119
Chemical hardness-driven interpretable machine learning approach for rapid search of photocatalysts
Kumar, R., & Singh, A. K. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00669-4 · Cited by 119
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Yi, Z., Liu, X. C., & Wei, R. (2022) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2022.103264 · Cited by 119
Lu, Z., Cheng, R., Jin, Y., Tan, K. C., & Deb, K. (2023) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3233364 · Cited by 129
Ren, K., Liu, J. P., Wu, Z., Liu, X., Nie, Y., & Xu, H. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122258 · Cited by 124
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Liao, W., Liu, Z., Dai, H., Xu, S., Wu, Z., Zhang, Y., Huang, X., Zhu, D., Cai, H., Liu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.11567 · Cited by 119
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Yue, S., Yang, Y., Wang, H., Rahul, H., & Katabi, D. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3397311 · Cited by 112
Olsson, H., Kartasalo, K., Mulliqi, N., Capuccini, M., Ruusuvuori, P., Samaratunga, H., Delahunt, B., Lindskog, C., Janssen, E. A. M., Blilie, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34945-8 · Cited by 111
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Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S. A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T., et al. (2020) · Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning · doi:10.1145/3399579.3399867 · Cited by 112
Duckworth, C., Chmiel, F. P., Burns, D. K., Zlatev, Z. D., White, N. M., Daniels, T. W. V., Kiuber, M., & Boniface, M. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-02481-y · Cited by 109
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Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107
Li, Q., Zhao, K., Bustamante, C. D., Ma, X., & Wong, W. H. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0439-8 · Cited by 106
Hu, C.-A., Chen, C.-M., Fang, Y.-C., Liang, S.-J., Wang, H.-C., Fang, W.-F., Sheu, C.-C., Perng, W.-C., Yang, K.-Y., Kao, K.-C., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-033898 · Cited by 106
Hill, B. L., Brown, R., Gabel, E., Rakocz, N., Lee, C., Cannesson, M., Baldi, P., Olde Loohuis, L., Johnson, R., Jew, B., et al. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.07.030 · Cited by 103
Artificial intelligence tools expand scientists' impact but contract science's focus
Hao, Q., Xu, F., Li, Y., & Evans, J. (2026) · Nature · doi:10.1038/s41586-025-09922-y · Cited by 117
Chindemi, G., Abdellah, M., Amsalem, O., Benavides-Piccione, R., Delattre, V., Doron, M., Ecker, A., Jaquier, A. T., King, J., Kumbhar, P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30214-w · Cited by 102
Tazawa, Y., Liang, K.-C., Yoshimura, M., Kitazawa, M., Kaise, Y., Takamiya, A., Kishi, A., Horigome, T., Mitsukura, Y., Mimura, M., et al. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03274 · Cited by 101
Taliaz, D., Spinrad, A., Barzilay, R., Barnett-Itzhaki, Z., Averbuch, D., Teltsh, O., Schurr, R., Darki-Morag, S., & Lerer, B. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01488-3 · Cited by 93
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Chen, Y., Wang, H., Lu, W., Wu, T., Yuan, W., Zhu, J., Lee, Y. K., Zhao, J., Zhang, H., & Chen, W. (2022) · Gut Microbes · doi:10.1080/19490976.2021.2025016 · Cited by 94
Shao, L., Liu, Z., Feng, L., Lou, X., Li, Z., Zhang, X.-Y., Wan, X., Zhou, X., Sun, K., Zhang, D.-F., et al. (2020) · Annals of Surgical Oncology · doi:10.1245/s10434-020-08659-4 · Cited by 92
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Zad, Z., Jiang, V. S., Wolf, A. T., Wang, T., Cheng, J. J., Paschalidis, I. C., & Mahalingaiah, S. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1298628 · Cited by 97
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King, Z., Farrington, J., Utley, M., Kung, E., Elkhodair, S., Harris, S., Sekula, R., Gillham, J., Li, K., & Crowe, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00649-y · Cited by 87
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Liu, Y., Ma, Z., Liu, X., Ma, S., Nepal, S., Deng, R. H., & Ren, K. (2020) · 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00017 · Cited by 93
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning
Schelter, S., Grafberger, S., & Dunning, T. (2021) · Proceedings of the 2021 International Conference on Management of Data · doi:10.1145/3448016.3457239 · Cited by 89
How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells
Liu, Y., Yan, W., Han, S., Zhu, H., Tu, Y., Guan, L., & Tan, X. (2022) · Solar RRL · doi:10.1002/solr.202101100 · Cited by 87
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Park, M. S., Son, H., Hyun, C., & Hwang, H. J. (2021) · IEEE Access · doi:10.1109/access.2021.3110270 · Cited by 83
Behravan, H., Hartikainen, J. M., Tengström, M., Pylkäs, K., Winqvist, R., Kosma, V., & Mannermaa, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-31573-5 · Cited by 75
Sheridan, R. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00825 · Cited by 84
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81
Yu, D., Liu, Z., Su, C., Han, Y., Duan, X., Zhang, R., Liu, X., Yang, Y., & Xu, S. (2020) · Thoracic Cancer · doi:10.1111/1759-7714.13204 · Cited by 80
Silvestri, S., Islam, S., Papastergiou, S., Tzagkarakis, C., & Ciampi, M. (2023) · Sensors · doi:10.3390/s23020651 · Cited by 76
Kaissis, G., Ziegelmayer, S., Lohöfer, F., Steiger, K., Algül, H., Muckenhuber, A., Yen, H.-Y., Rummeny, E., Friess, H., Schmid, R., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0218642 · Cited by 76
Li, W., Song, Y., Chen, K., Ying, J., Zheng, Z., Qiao, S., Yang, M., Zhang, M., & Zhang, Y. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-050989 · Cited by 76
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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting
Dobrojevic, M., Jovanovic, L., Babic, L., Cajic, M., Zivkovic, T., Zivkovic, M., Muthusamy, S., Antonijevic, M., & Bacanin, N. (2024) · Computers, Materials & Continua · doi:10.32604/cmc.2024.054459 · Cited by 75
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Tsuji, S., Hase, T., Yachie-Kinoshita, A., Nishino, T., Ghosh, S., Kikuchi, M., Shimokawa, K., Aburatani, H., Kitano, H., & Tanaka, H. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00826-3 · Cited by 75
Ji, I. H., Lee, J. H., Kang, M. J., Park, W. J., Jeon, S. H., & Seo, J. T. (2024) · Sensors · doi:10.3390/s24030898 · Cited by 74
Yang, X., Bian, J., Fang, R., Bjarnadottir, R. I., Hogan, W. R., & Wu, Y. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz144 · Cited by 74
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Lourie, N., Le Bras, R., & Choi, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17589 · Cited by 73
Li, L. (2019) · Remote Sensing · doi:10.3390/rs11111378 · Cited by 70
Lin, S., Hu, S., Song, W., Gu, M., Liu, J., Song, J., Liu, Z., Li, Z., Huang, K., Wu, Y., et al. (2022) · npj Flexible Electronics · doi:10.1038/s41528-022-00158-8 · Cited by 71
Shekhar, K., Whitney, I. E., Butrus, S., Peng, Y.-R., & Sanes, J. R. (2022) · eLife · doi:10.7554/elife.73809 · Cited by 68
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Borràs, D. M., Verbandt, S., Ausserhofer, M., Sturm, G., Lim, J., Verge, G. A., Vanmeerbeek, I., Laureano, R. S., Govaerts, J., Sprooten, J., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00605-4 · Cited by 67
Du, X., Novoa-Laurentiev, J., Plasek, J. M., Chuang, Y.-W., Wang, L., Marshall, G. A., Mueller, S. K., Chang, F., Datta, S., Paek, H., et al. (2024) · eBioMedicine · doi:10.1016/j.ebiom.2024.105401 · Cited by 69
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Bloch, L., Friedrich, C. M., & Initiative, F. T. A. D. N. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00879-4 · Cited by 68
Wang, P., & Ding, H. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103732 · Cited by 68
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De Hond, A., Raven, W., Schinkelshoek, L., Gaakeer, M., Ter Avest, E., Sir, O., Lameijer, H., Hessels, R. A., Reijnen, R., De Jonge, E., et al. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104496 · Cited by 66
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Creagh, A. P., Hamy, V., Yuan, H., Mertes, G., Tomlinson, R., Chen, W.-H., Williams, R., Llop, C., Yee, C., Duh, M. S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01013-y · Cited by 64
Ratigan, H. C., Krishnan, S., Smith, S., & Sheffield, M. E. J. (2023) · Nature Communications · doi:10.1038/s41467-023-42429-6 · Cited by 60
Chan, S., Hang, Y., Tong, C., Acquah, A., Schonfeldt, A., Gershuny, J., & Doherty, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03960-3 · Cited by 61
Ahmed, M. T., Rahman, M., Nur, S., Islam, A., & Das, D. (2021) · 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect49130.2021.9392608 · Cited by 60
Joseph, J. W., Leventhal, E. L., Grossestreuer, A. V., Wong, M. L., Joseph, L. J., Nathanson, L. A., Donnino, M. W., Elhadad, N., & Sanchez, L. D. (2020) · doi:10.1101/2020.05.02.20089052 · Cited by 57
Suri, J. S., Bhagawati, M., Paul, S., Protogerou, A. D., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A. M., Saxena, S., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030722 · Cited by 55
Martinc, M., Haider, F., Pollak, S., & Luz, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.642647 · Cited by 55
“…classifiers and k ∈ 10, 20, ..., 80 values, in which we employed 5 classifiers from the Scikit library (Pedregosa et al., 2011), namely Xgboost (Chen and Guestrin, 2016) (with 50 gradient boosted trees with max depth of 10), random forest (with 50 trees of max depth of 5), SVM (with linear…”
MethodSoltan, A. A. S., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M. A., Thickett, D. R., Fairhead, R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00272-7 · Cited by 54
Jiang, Z., Li, J., Kong, N., Kim, J.-H., Kim, B.-S., Lee, M.-J., Park, Y. M., Lee, S.-Y., Hong, S.-J., & Sul, J. H. (2022) · Scientific Reports · doi:10.1038/s41598-021-04373-7 · Cited by 54
Li, H.-C., Deng, Z.-Y., & Chiang, H.-H. (2020) · Sensors · doi:10.3390/s20216114 · Cited by 54
Stein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine · doi:10.1186/s13073-023-01261-9 · Cited by 53
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Al-Shehari, T., Al-Razgan, M., Alfakih, T., Alsowail, R. A., & Pandiaraj, S. (2023) · IEEE Access · doi:10.1109/access.2023.3326750 · Cited by 52
Müller, S. R., Chen, X., Peters, H., Chaintreau, A., & Matz, S. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-93087-x · Cited by 51
Kessler, R. C., Bauer, M. S., Bishop, T. M., Demler, O. V., Dobscha, S. K., Gildea, S. M., Goulet, J. L., Karras, E., Kreyenbuhl, J., Landes, S. J., et al. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00390 · Cited by 52
Deng, S., Zhu, Y., Yu, Y., & Huang, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121710 · Cited by 52
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Gu, S., Kelly, B. T., & Xiu, D. (2018) · The Review of Financial Studies · doi:10.2139/ssrn.3159577 · Cited by 52
Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2023) · arXiv · doi:10.48550/arxiv.2310.11324 · Cited by 52
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Chen, X., Qiu, W., Chen, L., Ma, Y., & Ma, J. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103881 · Cited by 51
Pruneski, J. A., Pareek, A., Nwachukwu, B. U., Martin, R. K., Kelly, B. T., Karlsson, J., Pearle, A. D., Kiapour, A. M., & Williams, R. J. (2023) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1007/s00167-022-07272-0 · Cited by 50
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Terra, A., Inam, R., Baskaran, S., Batista, P., Burdick, I., & Fersman, E. (2020) · GLOBECOM 2020 - 2020 IEEE Global Communications Conference · doi:10.1109/globecom42002.2020.9322496 · Cited by 50
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Berk, R. (2019) · Journal of Empirical Legal Studies · doi:10.1111/jels.12206 · Cited by 50
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Wang, S. C. Y., Nickel, G., Venkatesh, K. P., Raza, M. M., & Kvedar, J. C. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01034-7 · Cited by 50
Lessons and tips for designing a machine learning study using EHR data
Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46
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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