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Parallel Traversal of Large Ensembles of Decision Trees
2018
Zenodo
Machine-learnt models based on additive ensembles of regression trees are currently deemed the best solution to address complex classification, regression, and ranking tasks. The deployment of such models is computationally demanding: to compute the final prediction, the whole ensemble must be traversed by accumulating the contributions of all its trees. In particular, traversal cost impacts applications where the number of candidate items is large, the time budget available to apply the learnt
doi:10.5281/zenodo.2668379
fatcat:rtp6am3q2femfeweu7tkuknf64