Geodesic
Parallel implementation of a gradient boosted trees algorithm
Numerical methods and programming, Tome 14 (2013) no. 4, pp. 109-114.

Voir la notice de l'article provenant de la source Math-Net.Ru

The software implementation of a parallel gradient boosted trees algorithm that requires a distributed data storage and is intended mostly to large machine learning tasks is described. Computational experimental results are given to show an advantage in the performance and scalability of the proposed implementation over some other open-source implementations while using large datasets. Experimental quality evaluations are given also to show the competitiveness of the proposed implementation.
Keywords: decision tree; gradient boosting; parallel computation; MPI; distributed memory. 
@article{VMP_2013_14_4_a14,
     author = {P. N. Druzhkov},
     title = {Parallel implementation of a gradient boosted trees algorithm},
     journal = {Numerical methods and programming},
     pages = {109--114},
     publisher = {mathdoc},
     volume = {14},
     number = {4},
     year = {2013},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VMP_2013_14_4_a14/}
}
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P. N. Druzhkov. Parallel implementation of a gradient boosted trees algorithm. Numerical methods and programming, Tome 14 (2013) no. 4, pp. 109-114. http://geodesic.mathdoc.fr/item/VMP_2013_14_4_a14/