LightGBM: A highly efficient gradient boosting decision tree. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK( project of Microsoft.

References in zbMATH (referenced in 21 articles )

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  1. Karthick, K.; Aruna, S. K.; Samikannu, Ravi; Kuppusamy, Ramya; Teekaraman, Yuvaraja; Thelkar, Amruth Ramesh: Implementation of a heart disease risk prediction model using machine learning (2022)
  2. Sun, Xuxiang; Cao, Wenbo; Liu, Yilang; Zhu, Linyang; Zhang, Weiwei: High Reynolds number airfoil turbulence modeling method based on machine learning technique (2022)
  3. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  4. Arun S. Maiya: CausalNLP: A Practical Toolkit for Causal Inference with Text (2021) arXiv
  5. Atarashi, Kyohei; Oyama, Satoshi; Kurihara, Masahito: Factorization machines with regularization for sparse feature interactions (2021)
  6. Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu: AutoGL: A Library for Automated Graph Learning (2021) arXiv
  7. Nascimben, Mauro; Venturin, Manolo; Rimondini, Lia: Double-stage discretization approaches for biomarker-based bladder cancer survival modeling (2021)
  8. Saito, Miho; Ohsato, Takaya; Yamanaka, Suguru: An empirical evaluation of machine learning performance in corporate sales growth prediction (2021)
  9. Vargaftik, Shay; Keslassy, Isaac; Orda, Ariel; Ben-Itzhak, Yaniv: RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods (2021)
  10. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  11. Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek: dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python (2020) arXiv
  12. Lu, Haihao; Mazumder, Rahul: Randomized gradient boosting machine (2020)
  13. Miura, Kakeru; Ijitsu, Yasuyuki; Takekawa, Masahiro: The validation of statistical credit risk measurement by using business bank account activity data: an empirical analysis through machine learning (2020)
  14. Sage Hahn, Dekang Yuan, Wesley Thompson, Max M Owens, Nicholas Allgaier, Hugh Garavan: Brain Predictability toolbox: a Python library for neuroimaging based machine learning (2020) arXiv
  15. 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)
  16. Wen, Zeyi; Liu, Hanfeng; Shi, Jiashuai; Li, Qinbin; He, Bingsheng; Chen, Jian: ThunderGBM: fast GBDTs and random forests on GPUs (2020)
  17. Huan, Er-Yang; Wen, Gui-Hua: Multilevel and multiscale feature aggregation in deep networks for facial constitution classification (2019)
  18. Huber, Jakob; Müller, Sebastian; Fleischmann, Moritz; Stuckenschmidt, Heiner: A data-driven newsvendor problem: from data to decision (2019)
  19. Pei, Ziang; Cao, Shuangliang; Lu, Lijun; Chen, Wufan: Direct cellularity estimation on breast cancer histopathology images using transfer learning (2019)
  20. Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin: CatBoost: gradient boosting with categorical features support (2018) arXiv

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