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2020-11 - 1/1#
Parallelization of Random Forest predictions#
2020-11-27
I’ve been struggling to understand why the first implementation of TreeEnsemble could not get as fast as scikit-learn implementation for a RandomForest when the number of observations was 100.000 or above, 100 trees and a depth >= 10. The only difference was that the computation was parallelized by trees and not by observations. These observations are benchmarked in Benchmark Random Forests, Tree Ensemble, (AoS and SoA) (Benchmark Random Forests, Tree Ensemble, Multi-Classification for the multiclass version).
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