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DB Tsai commented on SPARK-10387: --------------------------------- Here is the current research result from us. We implemented a prototype of code generation for trees, and here is the implementation of code-gen. https://github.com/dbtsai/tree/blob/master/macros/src/main/scala/Tree.scala 1) We found the performance of code-gen is 4x to 6x faster than naive binary tree when the # of trees used in GBDT are small. But with around 500x trees, the performance is slightly worse. 2) We're also benchmarking the flatten trees idea described here, http://tullo.ch/articles/decision-tree-evaluation/ 3) Finally, QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees http://delivery.acm.org/10.1145/2770000/2767733/p73-lucchese.pdf is being implemented, we will benchmark it as well. > Code generation for decision tree > --------------------------------- > > Key: SPARK-10387 > URL: https://issues.apache.org/jira/browse/SPARK-10387 > Project: Spark > Issue Type: New Feature > Components: ML > Reporter: Xiangrui Meng > Assignee: DB Tsai > > Provide code generation for decision tree and tree ensembles. Let's first > discuss the design and then create new JIRAs for tasks. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org