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 15 articles )

Showing results 1 to 15 of 15.
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  1. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  2. Arun S. Maiya: CausalNLP: A Practical Toolkit for Causal Inference with Text (2021) arXiv
  3. 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
  4. Saito, Miho; Ohsato, Takaya; Yamanaka, Suguru: An empirical evaluation of machine learning performance in corporate sales growth prediction (2021)
  5. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  6. Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek: dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python (2020) arXiv
  7. Lu, Haihao; Mazumder, Rahul: Randomized gradient boosting machine (2020)
  8. 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
  9. 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)
  10. Wen, Zeyi; Liu, Hanfeng; Shi, Jiashuai; Li, Qinbin; He, Bingsheng; Chen, Jian: ThunderGBM: fast GBDTs and random forests on GPUs (2020)
  11. Huan, Er-Yang; Wen, Gui-Hua: Multilevel and multiscale feature aggregation in deep networks for facial constitution classification (2019)
  12. Huber, Jakob; Müller, Sebastian; Fleischmann, Moritz; Stuckenschmidt, Heiner: A data-driven newsvendor problem: from data to decision (2019)
  13. Pei, Ziang; Cao, Shuangliang; Lu, Lijun; Chen, Wufan: Direct cellularity estimation on breast cancer histopathology images using transfer learning (2019)
  14. Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin: CatBoost: gradient boosting with categorical features support (2018) arXiv
  15. Nalenz, Malte; Villani, Mattias: Tree ensembles with rule structured horseshoe regularization (2018)