And that’s fair. I’m always curious with how people are using the same or similar tools that my company uses. It’s good to hear common struggles or differences in workflows.
I suspect it's not that the included Watson Python didn't or wouldn't work but rather that the Watson-Python costs money (via paying for Watson) and Python-Python does not.
Given your perspective, a more important question might be what underlying algorithms are being used? I’m only a rookie data scientist but knowing that (for example) a random forest outperformed a neural net in this case, or even just a set of heuristics, is solid information. These can be built in Python, R, even C depending on the application, developer experience and a bunch of other stuff.
I'm looking at it from a couple of levels to support my data scientists. They're primarily python-based but we are adding newer data scientists who have experience with R. Algorithmically, we built a neural network for phenotyping.
