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
Keywords for this software
References in zbMATH (referenced in 123 articles )
Showing results 1 to 20 of 123.
Sorted by year (- Abolghasemi, Mahdi; Hyndman, Rob J.; Spiliotis, Evangelos; Bergmeir, Christoph: Model selection in reconciling hierarchical time series (2022)
- Höppner, Sebastiaan; Baesens, Bart; Verbeke, Wouter; Verdonck, Tim: Instance-dependent cost-sensitive learning for detecting transfer fraud (2022)
- Hornung, Roman; Boulesteix, Anne-Laure: Interaction forests: identifying and exploiting interpretable quantitative and qualitative interaction effects (2022)
- Huang, Shan; Ribers, Michael Allan; Ullrich, Hannes: Assessing the value of data for prediction policies: the case of antibiotic prescribing (2022)
- 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)
- Mao, Xiaojun; Peng, Liuhua; Wang, Zhonglei: Nonparametric feature selection by random forests and deep neural networks (2022)
- Nabavi, S. M.; Vahdani, Behnam; Nadjafi, B. Afshar; Adibi, M. A.: Synchronizing victim evacuation and debris removal: a data-driven robust prediction approach (2022)
- Aas, Kjersti; Jullum, Martin; Løland, Anders: Explaining individual predictions when features are dependent: more accurate approximations to Shapley values (2021)
- Adam Pocock: Tribuo: Machine Learning with Provenance in Java (2021) arXiv
- Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
- Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
- Antulov-Fantulin, Nino; Guo, Tian; Lillo, Fabrizio: Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume (2021)
- Arash Pakbin, Xiaochen Wang, Bobak J. Mortazavi, Donald K.K. Lee: BoXHED 2.0: Scalable boosting of functional data in survival analysis (2021) arXiv
- 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)
- Bertsimas, Dimitris; Dunn, Jack; Wang, Yuchen: Near-optimal nonlinear regression trees (2021)
- Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
- Chen, Shunqin; Guo, Zhengfeng; Zhao, Xinlei: Predicting mortgage early delinquency with machine learning methods (2021)
- Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
- Deval, Gaurav; Hamid, Faiz; Goel, Mayank: When to declare the third innings of a test cricket match? (2021)
- du Jardin, Philippe: Forecasting corporate failure using ensemble of self-organizing neural networks (2021)