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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Sorted by citations
Items 601–700 of 1,239
Predicting prime editing efficiency and product purity by deep learning
Mathis, N., Allam, A., Kissling, L., Marquart, K. F., Schmidheini, L., Solari, C., Balázs, Z., Krauthammer, M. O., & Schwank, G. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01613-7 · Cited by 167
Li, Y., Zhou, Y., Zhang, Y., Zhong, L., Wang, J., & Chen, J. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.02.013 · Cited by 170
MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction
Cao, Z., Magar, R., Wang, Y., & Farimani, A. B. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11420 · Cited by 176
Leng, J., Zhu, X., Huang, Z., Xu, K., Liu, Z., Liu, Q., & Chen, X. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3257172 · Cited by 171
Cui, X., Lee, M., Koo, C., & Hong, T. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.113997 · Cited by 166
Liu, B., Zhu, C., Li, G., Zhang, W., Lai, J., Tang, R., He, X., Li, Z., & Yu, Y. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403314 · Cited by 171
Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED
Goto, T., Camargo, C. A., Faridi, M. K., Yun, B. J., & Hasegawa, K. (2018) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2018.06.062 · Cited by 164
Gulick, S. P. S., Bralower, T. J., Ormö, J., Hall, B., Grice, K., Schaefer, B., Lyons, S. L., Freeman, K. H., MORGAN, J. V., Artemieva, N. A., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1909479116 · Cited by 164
Using data mining techniques for bike sharing demand prediction in metropolitan city
Easwaramoorthy, S. V., Park, J., & Cho, Y. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.007 · Cited by 165
Kumar, P., Kumar, R., Gupta, G. P., & Tripathi, R. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4112 · Cited by 166
Guo, Y., He, X., Su, Y., Dai, Y., Xie, M., Yang, S., Chen, J., Wang, K., Zhou, D., & Wang, C. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c00339 · Cited by 164
MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction
Zhang, X., Wu, C., Yang, Z., Wu, Z.-X., Yi, J., Hsieh, C., Hou, T., & Cao, D. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab152 · Cited by 164
Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP
Alabi, R. O., Elmusrati, M. S., Leivo, I. V. V., Almangush, A., & Mäkitie, A. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-35795-0 · Cited by 164
Machine‐Learning‐Aided Advanced Electrochemical Biosensors
Bocan, A., Moakhar, R. S., Mata, C. D. R., Petkun, M., Iure‐Grimmel, T. D., Yedire, S. G., Shieh, H., Jahromi, A. K., Mahshid, S., & Mahshid, S. (2025) · Advanced Materials · doi:10.1002/adma.202417520 · Cited by 174
Chen, J., Yin, J., Zang, L., Zhang, T., & Zhao, M. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134021 · Cited by 164
Katongtung, T., Onsree, T., & Tippayawong, N. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126278 · Cited by 163
Gibson, P. B., Chapman, W. E., Altınok, A., Monache, L. D., DeFlorio, M. J., & Waliser, D. E. (2021) · Communications Earth & Environment · doi:10.1038/s43247-021-00225-4 · Cited by 181
Mind the gap: Performance metric evaluation in brain‐age prediction
de Lange, A. G., Anatürk, M., Rokicki, J., Han, L. K. M., Franke, K., Alnæs, D., Ebmeier, K. P., Draganski, B., Kaufmann, T., Westlye, L. T., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.25837 · Cited by 161
Where Medical Statistics Meets Artificial Intelligence
Hunter, D. J., & Holmes, C. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2212850 · Cited by 169
Hou, J., Xu, J., Lin, C., Jiang, D., & Mei, X. (2024) · Energy · doi:10.1016/j.energy.2023.130056 · Cited by 162
Yuan, X., Chen, J., Yang, J., Zhang, N., Yang, T., Han, T., & Taherkordi, A. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3157056 · Cited by 178
Bedi, P., Gupta, N., & Jindal, V. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01886-y · Cited by 171
Wei, L., He, W., Malik, A., Su, R., Cui, L., & Manavalan, B. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa275 · Cited by 162
Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides
Xu, J., Li, F., Leier, A., Xiang, D., Shen, H., Marquez‐Lago, T. T., Li, J., Yu, D., & Song, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab083 · Cited by 173
Deng, X., Li, M., Deng, S., & Wang, L. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02476-x · Cited by 161
Kumarasan, Y., Rajandas, H., Parimannan, S., Ravichandran, M., Marimuthu, K., Petersen, B., Clokie, M. R. J., Millard, A. D., & Sicheritz‐Pontén, T. (2022) · Viruses · doi:10.3390/v14020342 · Cited by 161
MLP-PSO Hybrid Algorithm for Heart Disease Prediction
Bataineh, A. A., & Manacek, S. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081208 · Cited by 175
Zhang, W., Wu, C., Tang, L., Gu, X., & Wang, L. (2022) · Gondwana Research · doi:10.1016/j.gr.2022.10.004 · Cited by 161
Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost
Szczepanek, R. (2022) · Hydrology · doi:10.3390/hydrology9120226 · Cited by 172
Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach
Climent, F., Momparler, A., & Carmona, P. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.11.015 · Cited by 160
Ge, X., Ding, J., Jin, X., Wang, J., Chen, X., Li, X., Liu, J., & Xie, B. (2021) · Remote Sensing · doi:10.3390/rs13081562 · Cited by 160
Patel, S. K., Surve, J., Katkar, V. D., Parmar, J., Al-Zahrani, F. A., Ahmed, K., & Bui, F. M. (2022) · IEEE Access · doi:10.1109/access.2022.3154386 · Cited by 160
Du-Bus: A Realtime Bus Waiting Time Estimation System Based On Multi-Source Data
Rong, Y., Xu, Z., Liu, J., Liu, H., Ding, J., Liu, X., Luo, W., Zhang, C., & Gao, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3210170 · Cited by 160
Practical guidelines for the use of gradient boosting for molecular property prediction
Boldini, D., Grisoni, F., Kühn, D., Friedrich, L., & Sieber, S. A. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00743-7 · Cited by 202
Mohamadlou, H., Lynn-Palevsky, A., Barton, C., Chettipally, U. K., Shieh, L. Y., Calvert, J. S., Saber, N., & Das, R. (2018) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358118776326 · Cited by 168
Ke, G., Xu, Z., Zhang, J., Bian, J., & Liu, T. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330858 · Cited by 166
Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer
Liu, P., Fu, B., Yang, S. X., Deng, L., Zhong, X., & Zheng, H. Y. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2993278 · Cited by 165
Kumar, P., Gupta, G. P., & Tripathi, R. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02696-3 · Cited by 161
Jong, S. C., Ong, D. E. L., & Oh, E. (2021) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2021.103946 · Cited by 170
Shi, D., Zhou, F., Mu, W., Cheng, L., Mu, T., Yu, G., & Li, R. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp03423a · Cited by 166
Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F., Tan, Y., Gan, V. J. L., & Wan, Z. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118955 · Cited by 160
Meli, R., Morris, G. M., & Biggin, P. C. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.885983 · Cited by 176
A geometric deep learning framework for drug repositioning over heterogeneous information networks
Zhao, B.-W., Su, X., Hu, P., Ma, Y.-P., Zhou, X., & Hu, L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac384 · Cited by 164
Zhong, S., & Guan, X. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c02198 · Cited by 163
Kumar, R., Arora, R., Bansal, V., Sahayasheela, V. J., Buckchash, H., Imran, J., Narayanan, N., Pandian, G. N., & Raman, B. (2020) · medRxiv · doi:10.1101/2020.04.13.20063461 · Cited by 158
Tran, M.-Q., Elsisi, M., Mahmoud, K., Liu, M.-K., Lehtonen, M., & Darwish, M. M. F. (2021) · IEEE Access · doi:10.1109/access.2021.3105297 · Cited by 158
Guryanov, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37334-4_4 · Cited by 157
Zhong, S., Zhang, K., Wang, D., & Zhang, H. J. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.126627 · Cited by 153
Prediction of melt pool temperature in directed energy deposition using machine learning
Zhang, Z., Liu, Z., & Wu, D. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101692 · Cited by 163
Comparative evaluation of machine learning models for groundwater quality assessment
Bedi, S., Samal, A. K., Ray, C., & Snow, D. D. (2020) · Environmental Monitoring and Assessment · doi:10.1007/s10661-020-08695-3 · Cited by 164
Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study
Alkesaiberi, A., Harrou, F., & Sun, Y. (2022) · Energies · doi:10.3390/en15072327 · Cited by 171
Ullah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2022) · Travel Behaviour and Society · doi:10.1016/j.tbs.2022.11.006 · Cited by 157
Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., & Johansson, C. (2018) · Energy · doi:10.1016/j.energy.2018.05.111 · Cited by 155
Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning
Loggenberg, K., Strever, A. E., Greyling, B. T., & Poona, N. (2018) · Remote Sensing · doi:10.3390/rs10020202 · Cited by 161
Jiang, Y., Cao, S., Cao, S., Chen, J., Wang, G., Shi, W., Deng, Y.-N., Cheng, N., Ma, K., Zeng, K., et al. (2020) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-020-03366-9 · Cited by 156
Jeong, N., Chung, T.-H., & Tong, T. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c04041 · Cited by 157
Machine-Learning-Guided Discovery of 19 F MRI Agents Enabled by Automated Copolymer Synthesis
Reis, M. H., Gusev, F., Taylor, N. G., Chung, S. H., Verber, M., Lee, Y. Z., Isayev, O., & Leibfarth, F. A. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c08181 · Cited by 162
Zhang, W., He, Y., Wang, L., Liu, S., & Meng, X. (2023) · Geological Journal · doi:10.1002/gj.4683 · Cited by 162
Radiomics and Its Feature Selection: A Review
Zhang, W., Guo, Y. T., & Jin, Q. (2023) · Symmetry · doi:10.3390/sym15101834 · Cited by 170
Calabrese, M., Cimmino, M., Fiume, F., Manfrin, M., Romeo, L., Ceccacci, S., Paolanti, M., Toscano, G., Ciandrini, G., Carrotta, A., et al. (2020) · Information · doi:10.3390/info11040202 · Cited by 158
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157
Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods
Choudhary, K. K., Bercx, M., Jiang, J., Pachter, R., Lamoen, D., & Tavazza, F. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b02166 · Cited by 156
Jiang, M., Liu, J., Zhang, L., & Liu, C. (2020) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122272 · Cited by 155
XGBoost Model and Its Application to Personal Credit Evaluation
Li, H., Cao, Y., Li, S., Zhao, J., & Sun, Y. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2972533 · Cited by 155
Machine learning in the prediction of cancer therapy
Rafique, R., Islam, S. M. R., & Kazi, J. U. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.003 · Cited by 156
An effective detection approach for phishing websites using URL and HTML features
Aljofey, A., Jiang, Q., Rasool, A., Chen, H., Liu, W., Qu, Q., & Wang, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-10841-5 · Cited by 155
Drug screening at single-organoid resolution via bioprinting and interferometry
Tebon, P. J., Wang, B., Markowitz, A. L., Davarifar, A., Tsai, B. L., Krawczuk, P., Gonzalez, A. E., Sartini, S., Murray, G. F., Nguyen, H. T. L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38832-8 · Cited by 163
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Cao, B., Zheng, L., Zhang, C., Yu, P. S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., & Leow, A. D. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098086 · Cited by 145
“• XGB: The implementation of a tree boosting system from XGBoost6 [10] is used.”
MethodSmart Contract Security: A Software Lifecycle Perspective
Huang, Y., Bian, Y., Li, R., Zhao, J. L., & Shi, P. (2019) · IEEE Access · doi:10.1109/access.2019.2946988 · Cited by 154
Machine learning techniques for protein function prediction
Bonetta, R., & Valentino, G. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25832 · Cited by 161
Pham, T. D., Le, N. N., Ha, N. T., Nguyen, L. V., Xia, J., Yokoya, N., To, T. T., Trinh, H. X., Kieu, Q. L., & Takeuchi, W. (2020) · Remote Sensing · doi:10.3390/rs12050777 · Cited by 154
Zhang, J., Cheng, T., Guo, W. F., Xu, X., Qiao, H., Xie, Y., & Ma, X. (2021) · Plant Methods · doi:10.1186/s13007-021-00750-5 · Cited by 154
Modelling heating and cooling energy demand for building stock using a hybrid approach
Li, X., & Yao, R. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110740 · Cited by 160
Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing
Tian, Y., Huang, H., Zhou, G., Zhang, Q., Tao, J., Zhang, Y., & Lin, J. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.146816 · Cited by 158
Shi, C., & Wang, Y. (2021) · Journal of Geotechnical and Geoenvironmental Engineering · doi:10.1061/(asce)gt.1943-5606.0002583 · Cited by 154
Water Quality Prediction Using KNN Imputer and Multilayer Perceptron
Juna, A., Umer, M., Sadiq, S., Karamti, H., Eshmawi, A. A., Mohamed, A., & Ashraf, I. (2022) · Water · doi:10.3390/w14172592 · Cited by 154
Chatzimparmpas, A., Martins, R. M., Kucher, K., & Kerren, A. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030352 · Cited by 164
Machine learning-guided discovery and design of non-hemolytic peptides
Plisson, F., Ramírez‐Sánchez, O., & Martínez-Hernández, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-73644-6 · Cited by 153
Research on Intrusion Detection Based on Particle Swarm Optimization in IoT
Liu, J., Yang, D., Lian, M., & Li, M. (2021) · IEEE Access · doi:10.1109/access.2021.3063671 · Cited by 153
A hybrid DNN–LSTM model for detecting phishing URLs
Özcan, A., Catal, C., Dönmez, E., & Senturk, B. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06401-z · Cited by 178
The proteogenomic subtypes of acute myeloid leukemia
Jayavelu, A. K., Wolf, S., Buettner, F., Alexe, G., Häupl, B., Comoglio, F., Schneider, C., Doebele, C., Fuhrmann, D. C., Wagner, S., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.02.006 · Cited by 158
Akerberg, B. N., Gu, F., VanDusen, N. J., Zhang, X., Dong, R., Li, K., Zhang, B., Zhou, B., Sethi, I., Ma, Q., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12812-3 · Cited by 152
Si, M., Ying, X., Du, S., & Du, K. (2020) · Atmospheric measurement techniques · doi:10.5194/amt-13-1693-2020 · Cited by 168
A Comparative Performance Assessment of Ensemble Learning for Credit Scoring
Li, Y., & Chen, W. (2020) · Mathematics · doi:10.3390/math8101756 · Cited by 159
Kiangala, K. S., & Wang, Z. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100024 · Cited by 178
Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction
Saber, M., Boulmaiz, T., Guermoui, M., Abdrabo, K. I., Kantoush, S. A., Sumi, T., Boutaghane, H., Nohara, D., & Mabrouk, E. (2021) · Geocarto International · doi:10.1080/10106049.2021.1974959 · Cited by 155
Click-through rate prediction in online advertising: A literature review
Yang, Y., & Zhai, P. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2021.102853 · Cited by 178
Sharmin, S., Ahammad, T., Talukder, M. A., & Ghose, P. (2023) · IEEE Access · doi:10.1109/access.2023.3304628 · Cited by 152
Wang, S., & Peng, H. Q. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130650 · Cited by 152
Perspective: Identification of collective variables and metastable states of protein dynamics
Sittel, F., & Stock, G. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5049637 · Cited by 149
Kardani, N., Zhou, A., Nazem, M., & Shen, S. (2019) · Geotechnical and Geological Engineering · doi:10.1007/s10706-019-01085-8 · Cited by 151
Naghibi, S. A., Hashemi, H., Berndtsson, R., & Lee, S. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125197 · Cited by 151
Sikder, N., Masud, M., Bairagi, A. K., Arif, A. S. M., Nahid, A.-A., & Alhumyani, H. A. (2021) · Symmetry · doi:10.3390/sym13040670 · Cited by 151
A Comprehensive Study of Random Forest for Short-Term Load Forecasting
Dudek, G. (2022) · Energies · doi:10.3390/en15207547 · Cited by 151
Majority of global river flow sustained by groundwater
Xie, J., Liu, X., Jasechko, S., Berghuijs, W. R., Wang, K., Liu, C., Reichstein, M., Jung, M., & Koirala, S. (2024) · Nature Geoscience · doi:10.1038/s41561-024-01483-5 · Cited by 165
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
Watson, D. (2019) · Minds and Machines · doi:10.1007/s11023-019-09506-6 · Cited by 148
Pielot, M., Cardoso, B., Katevas, K., Serrà, J., Matic, A., & Oliver, N. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130956 · Cited by 149
Aaboud, M., Aad, G., Abbott, B., Abdinov, O., Abeloos, B., Abidi, S. H., AbouZeid, O. S., Abraham, N. L., Abramowicz, H., Abreu, H., et al. (2018) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.97.072003 · Cited by 150
Wang, D., Zhang, Y., & Zhao, Y. (2017) · International Conference on Computational Biology and Bioinformatics (ICCBB) · doi:10.1145/3155077.3155079 · Cited by 150
A translucent box: interpretable machine learning in ecology
Lucas, T. C. D. (2020) · Ecological Monographs · doi:10.1002/ecm.1422 · Cited by 161
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
