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Are there any good sources that explain how decision trees can be implemented in a scalable way on a distributed computing system. Where in a given source is this explained?

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Apache Spark can do it, using the new MLLib library. Here's a presentation, and here are some benchmarks. Bindings are available for python, scala, and java.

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You should read the paper from Google on PLANET, which was their distributed MapReduce-based implementation of random decision forests: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36296.pdf

You may or may not like the architecture but there are a number of interesting ideas about scaling up here.

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