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 102 articles )

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  1. Höppner, Sebastiaan; Baesens, Bart; Verbeke, Wouter; Verdonck, Tim: Instance-dependent cost-sensitive learning for detecting transfer fraud (2022)
  2. Aas, Kjersti; Jullum, Martin; Løland, Anders: Explaining individual predictions when features are dependent: more accurate approximations to Shapley values (2021)
  3. Adam Pocock: Tribuo: Machine Learning with Provenance in Java (2021) arXiv
  4. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  5. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  6. Arash Pakbin, Xiaochen Wang, Bobak J. Mortazavi, Donald K.K. Lee: BoXHED 2.0: Scalable boosting of functional data in survival analysis (2021) arXiv
  7. 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)
  8. Bertsimas, Dimitris; Dunn, Jack; Wang, Yuchen: Near-optimal nonlinear regression trees (2021)
  9. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  10. Chen, Shunqin; Guo, Zhengfeng; Zhao, Xinlei: Predicting mortgage early delinquency with machine learning methods (2021)
  11. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  12. Deval, Gaurav; Hamid, Faiz; Goel, Mayank: When to declare the third innings of a test cricket match? (2021)
  13. 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)
  14. du Jardin, Philippe: Forecasting corporate failure using ensemble of self-organizing neural networks (2021)
  15. du Jardin, Philippe: Forecasting bankruptcy using biclustering and neural network-based ensembles (2021)
  16. Fermanian, Adeline: Embedding and learning with signatures (2021)
  17. Fernández, Javier; Bornn, Luke; Cervone, Daniel: A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions (2021)
  18. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  19. 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)
  20. Gunnarsson, Björn Rafn; vanden Broucke, Seppe; Baesens, Bart; Óskarsdóttir, María; Lemahieu, Wilfried: Deep learning for credit scoring: do or don’t? (2021)

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