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I have used several times. Uploaded a CSV file, thrown neural networks or random decision forests at it to see how well it worked. It works well when you are p

I have used several times. Uploaded a CSV file, thrown neural networks or random decision forests at it to see how well it worked. It works well when you are prototyping and is quite accessible. It is also good to test various algorithms and the GUI enables you to get a good sense 'flow of data' (input, filters, algorithms, comparisons, output, etc.). However, it becomes painful really quickly, and it is not for production! For the particular project, I ended up using RDFs (because they showed the most promising results in Azure), and implemented RDFs by hand. After some tweaking, my own implementation performed better than the one on Azure, so I was satisfied with that. BTW. RDFs are quite easy to implement! I think, I'll switch to TensorFlow for equivalent future prototyping. Mainly, because I can run it locally and the script (Python) support on Azure ML is terrible.

Avalaxy 10y ago
Interesting experience, thanks! > For the particular project, I ended up using RDFs (because they showed the most promising results in Azure), and implemented RDFs by hand. After some tweaking, my own implementation performed better than the one on Azure, so I was satisfied with that. What language/libraries did you implement this in? Is this Python? Do you have any idea what makes it faster than Azure?
- joakleaf 10y ago
  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.

Avalaxy 10y ago

Interesting experience, thanks! > For the particular project, I ended up using RDFs (because they showed the most promising results in Azure), and implemented RDFs by hand. After some tweaking, my own implementation performed better than the one on Azure, so I was satisfied with that. What language/libraries did you implement this in? Is this Python? Do you have any idea what makes it faster than Azure?

joakleaf 10y ago
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.