XGBoost

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples


References in zbMATH (referenced in 142 articles )

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  1. Abolghasemi, Mahdi; Hyndman, Rob J.; Spiliotis, Evangelos; Bergmeir, Christoph: Model selection in reconciling hierarchical time series (2022)
  2. Adomavicius, Gediminas; Wang, Yaqiong: Improving reliability estimation for individual numeric predictions: a machine learning approach (2022)
  3. Bastos, João A.; Matos, Sara M.: Explainable models of credit losses (2022)
  4. Bertsimas, Dimitris; Digalakis, Vassilis jun.: The backbone method for ultra-high dimensional sparse machine learning (2022)
  5. Chandna, Akshat; Srinivasan, Sanjay: Mapping natural fracture networks using geomechanical inferences from machine learning approaches (2022)
  6. Dubois, Pierre; Gomez, Thomas; Planckaert, Laurent; Perret, Laurent: Machine learning for fluid flow reconstruction from limited measurements (2022)
  7. Höppner, Sebastiaan; Baesens, Bart; Verbeke, Wouter; Verdonck, Tim: Instance-dependent cost-sensitive learning for detecting transfer fraud (2022)
  8. Hornung, Roman; Boulesteix, Anne-Laure: Interaction forests: identifying and exploiting interpretable quantitative and qualitative interaction effects (2022)
  9. Huang, Shan; Ribers, Michael Allan; Ullrich, Hannes: Assessing the value of data for prediction policies: the case of antibiotic prescribing (2022)
  10. Kang, Yanfei; Cao, Wei; Petropoulos, Fotios; Li, Feng: Forecast with forecasts: diversity matters (2022)
  11. Karthick, K.; Aruna, S. K.; Samikannu, Ravi; Kuppusamy, Ramya; Teekaraman, Yuvaraja; Thelkar, Amruth Ramesh: Implementation of a heart disease risk prediction model using machine learning (2022)
  12. Kong, Wenjia; Li, Haochen; Yu, Chen; Xia, Jiangjiang; Kang, Yanyan; Zhang, Pingwen: A deep spatio-temporal forecasting model for multi-site weather prediction post-processing (2022)
  13. Lellep, Martin; Prexl, Jonathan; Eckhardt, Bruno; Linkmann, Moritz: Interpreted machine learning in fluid dynamics: explaining relaminarisation events in wall-bounded shear flows (2022)
  14. Liu, Siyuan; Tang, Shaojie; Zheng, Jiangchuan; Ni, Lionel M.: Unsupervised learning for human mobility behaviors (2022)
  15. Mao, Xiaojun; Peng, Liuhua; Wang, Zhonglei: Nonparametric feature selection by random forests and deep neural networks (2022)
  16. Nabavi, S. M.; Vahdani, Behnam; Nadjafi, B. Afshar; Adibi, M. A.: Synchronizing victim evacuation and debris removal: a data-driven robust prediction approach (2022)
  17. Teng, Long: Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations (2022)
  18. Uddin, Ajim; Tao, Xinyuan; Chou, Chia-Ching; Yu, Dantong: Are missing values important for earnings forecasts? A machine learning perspective (2022)
  19. Wang, Huaduo; Gupta, Gopal: FOLD-R++: a scalable toolset for automated inductive learning of default theories from mixed data (2022)
  20. Wang, Kanix; Hussain, Walid; Birge, John R.; Schreiber, Michael D.; Adelman, Daniel: A high-fidelity model to predict length of stay in the neonatal intensive care unit (2022)

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