Java, actually... But not by choice -- The client requested that the project was written in Java.

It is always hard to compare these algorithms because you have lots of parameters, and I must admit, that I did not tweak the settings on Azure.

I don't know why (or indeed if) it was faster/better results. You cannot really time the algorithms on Azure the same way you can locally. But because I ran my own implementation on a fast local machine it probably felt a lot faster -- This and then that my implementation was more specific to the overall problem.

In any case, speed when constructing the RDFs is not the main concern -- It is the quality of the model you create. Here I could add some specific tweaks for the particular problem (e.g. weighing different classes) that were not available in Azure.

Finally, using the RDFs for classification is extremely fast and straight-forward (basically you just branch through a large set of trees), and were nowhere near the biggest bottleneck in the specific project.

It just works a whole lot better locally.