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 123 articles )

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  1. Abolghasemi, Mahdi; Hyndman, Rob J.; Spiliotis, Evangelos; Bergmeir, Christoph: Model selection in reconciling hierarchical time series (2022)
  2. Höppner, Sebastiaan; Baesens, Bart; Verbeke, Wouter; Verdonck, Tim: Instance-dependent cost-sensitive learning for detecting transfer fraud (2022)
  3. Hornung, Roman; Boulesteix, Anne-Laure: Interaction forests: identifying and exploiting interpretable quantitative and qualitative interaction effects (2022)
  4. Huang, Shan; Ribers, Michael Allan; Ullrich, Hannes: Assessing the value of data for prediction policies: the case of antibiotic prescribing (2022)
  5. 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)
  6. Mao, Xiaojun; Peng, Liuhua; Wang, Zhonglei: Nonparametric feature selection by random forests and deep neural networks (2022)
  7. Nabavi, S. M.; Vahdani, Behnam; Nadjafi, B. Afshar; Adibi, M. A.: Synchronizing victim evacuation and debris removal: a data-driven robust prediction approach (2022)
  8. Aas, Kjersti; Jullum, Martin; Løland, Anders: Explaining individual predictions when features are dependent: more accurate approximations to Shapley values (2021)
  9. Adam Pocock: Tribuo: Machine Learning with Provenance in Java (2021) arXiv
  10. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  11. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  12. Antulov-Fantulin, Nino; Guo, Tian; Lillo, Fabrizio: Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume (2021)
  13. Arash Pakbin, Xiaochen Wang, Bobak J. Mortazavi, Donald K.K. Lee: BoXHED 2.0: Scalable boosting of functional data in survival analysis (2021) arXiv
  14. Aziz, Wajid; Hussain, Lal; Khan, Ishtiaq Rasool; Alowibdi, Jalal S.; Alkinani, Monagi H.: Machine learning based classification of normal, slow and fast walking by extracting multimodal features from stride interval time series (2021)
  15. Bertsimas, Dimitris; Dunn, Jack; Wang, Yuchen: Near-optimal nonlinear regression trees (2021)
  16. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  17. Chen, Shunqin; Guo, Zhengfeng; Zhao, Xinlei: Predicting mortgage early delinquency with machine learning methods (2021)
  18. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  19. Deval, Gaurav; Hamid, Faiz; Goel, Mayank: When to declare the third innings of a test cricket match? (2021)
  20. du Jardin, Philippe: Forecasting corporate failure using ensemble of self-organizing neural networks (2021)

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