R package xgboost. Extreme Gradient Boosting, which is an efficient implementation of gradient boosting framework. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

References in zbMATH (referenced in 20 articles )

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  1. Arkajyoti Saha, Sumanta Basu, Abhirup Datta: RandomForestsGLS: An R package for Random Forests for dependent data (2022) not zbMATH
  2. Ermağan, Umut; Yıldız, Barış; Salman, F. Sibel: A learning based algorithm for drone routing (2022)
  3. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  4. Lekivetz, Ryan; Morgan, Joseph: On the testing of statistical software (2021)
  5. Page, Garritt L.; Quintana, Fernando A.; Rosner, Gary L.: Discovering interactions using covariate informed random partition models (2021)
  6. Begüm D. Topçuoğlu; Zena Lapp; Kelly L. Sovacool; Evan Snitkin; Jenna Wiens; Patrick D. Schloss: mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines (2020) not zbMATH
  7. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  8. Fang, Jie; Lin, Jianwu; Xia, Shutao; Xia, Zhikang; Hu, Shenglei; Liu, Xiang; Jiang, Yong: Neural network-based automatic factor construction (2020)
  9. Lekivetz, Ryan; Morgan, Joseph: Covering arrays: using prior information for construction, evaluation and to facilitate fault localization (2020)
  10. Orhobor, Oghenejokpeme I.; Alexandrov, Nickolai N.; King, Ross D.: Predicting Rice phenotypes with meta and multi-target learning (2020)
  11. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
  12. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  13. Shah, Rajen D.; Peters, Jonas: The hardness of conditional independence testing and the generalised covariance measure (2020)
  14. Tomita, Tyler M.; Browne, James; Shen, Cencheng; Chung, Jaewon; Patsolic, Jesse L.; Falk, Benjamin; Priebe, Carey E.; Yim, Jason; Burns, Randal; Maggioni, Mauro; Vogelstein, Joshua T.: Sparse projection oblique randomer forests (2020)
  15. Jaeger, Byron C.; Long, D. Leann; Long, Dustin M.; Sims, Mario; Szychowski, Jeff M.; Min, Yuan-I; McClure, Leslie A.; Howard, George; Simon, Noah: Oblique random survival forests (2019)
  16. Sellereite, Nikolai; Jullum, Martin: shapr: An R-package for explaining machine learning models with dependence-aware Shapley values (2019) not zbMATH
  17. Yuan Tang: Autoplotly - Automatic Generation of Interactive Visualizations for Popular Statistical Results (2018) arXiv
  18. Andrew Sohn, Randal S. Olson, Jason H. Moore: Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming (2017) arXiv
  19. Podusenko, Albert; Nikulin, Vsevolod; Tanev, Ivan; Shimohara, Katsunori: Comparative analysis of classifiers for classification of emergency braking of road motor vehicles (2017)
  20. Thomas Keck: FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification (2016) arXiv