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 79 articles )

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  1. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  2. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  3. Arash Pakbin, Xiaochen Wang, Bobak J. Mortazavi, Donald K.K. Lee: BoXHED 2.0: Scalable boosting of functional data in survival analysis (2021) arXiv
  4. 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)
  5. Bertsimas, Dimitris; Dunn, Jack; Wang, Yuchen: Near-optimal nonlinear regression trees (2021)
  6. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  7. Chen, Shunqin; Guo, Zhengfeng; Zhao, Xinlei: Predicting mortgage early delinquency with machine learning methods (2021)
  8. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  9. Ding, Chenchen; Han, Haitao; Li, Qianyue; Yang, Xiaoxia; Liu, Taigang: iT3SE-PX: identification of bacterial type III secreted effectors using PSSM profiles and XGBoost feature selection (2021)
  10. du Jardin, Philippe: Forecasting corporate failure using ensemble of self-organizing neural networks (2021)
  11. Fermanian, Adeline: Embedding and learning with signatures (2021)
  12. Gossmann, Alexej; Pezeshk, Aria; Wang, Yu-Ping; Sahiner, Berkman: Test data reuse for the evaluation of continuously evolving classification algorithms using the area under the receiver operating characteristic curve (2021)
  13. Ma, Shaohui; Fildes, Robert: Retail sales forecasting with meta-learning (2021)
  14. Poonawala, Hasan A.; Lauffer, Niklas; Topcu, Ufuk: Training classifiers for feedback control with safety in mind (2021)
  15. Yue Zhao, Zhi Qiao, Cao Xiao, Lucas Glass, Jimeng Sun: PyHealth: A Python Library for Health Predictive Models (2021) arXiv
  16. Zhang, Dan; Chen, Hua-Dong; Zulfiqar, Hasan; Yuan, Shi-Shi; Huang, Qin-Lai; Zhang, Zhao-Yue; Deng, Ke-Jun: iBLP: an XGBoost-based predictor for identifying bioluminescent proteins (2021)
  17. Benkeser, David; Petersen, Maya; van der Laan, Mark J.: Improved small-sample estimation of nonlinear cross-validated prediction metrics (2020)
  18. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  19. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  20. Bullock, Joseph; Luccioni, Alexandra; Pham, Katherine Hoffman; Lam, Cynthia Sin Nga; Luengo-Oroz, Miguel: Mapping the landscape of artificial intelligence applications against COVID-19 (2020)

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