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Is it possible to add boosted random forest feature? Similar to the xgboost feature num_parallel_tree. This will speed up the training time for the problems with a small number of features (in which using higher values of num_thread not making things faster because of overhead). If we can train couple of trees at the same time and use their averages for boosting that will make it possible to achieve similar accuracy in shorter time for the problems with small number of features. One more suggestion for the implementation, I think num_thread should only used for generating one tree just as now and the parallel trees should create its own parallel threads with a different parameter (or num_parallel_tree can used for this purpose as well) , otherwise the overhead problem emerges again, which is the case in current xgboost implementation.
Thanks,
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Is it possible to add boosted random forest feature? Similar to the xgboost feature num_parallel_tree. This will speed up the training time for the problems with a small number of features (in which using higher values of num_thread not making things faster because of overhead). If we can train couple of trees at the same time and use their averages for boosting that will make it possible to achieve similar accuracy in shorter time for the problems with small number of features. One more suggestion for the implementation, I think num_thread should only used for generating one tree just as now and the parallel trees should create its own parallel threads with a different parameter (or num_parallel_tree can used for this purpose as well) , otherwise the overhead problem emerges again, which is the case in current xgboost implementation.
Thanks,
The text was updated successfully, but these errors were encountered: