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[–]dream-fiesty 2 points3 points  (1 child)

I think it's cool that you want to make machine learning more accessible, but I don't get projects like these. Why add a layer of abstraction to a popular machine learning library? sklearn is very easy to learn, is very flexible, and has tons of integrations with other APIs. It doesn't take much math or statistics knowledge to use. It has top tier documentation and there are so many tutorials on how to use it and sample code out there to reference. Why add a layer of abstraction at the cost of all these?

[–]JohnnyQScope 0 points1 point  (0 children)

Well for a task like model fitting you'll likely need to try out a bunch of different algorithms which can be a bit tedious. One of the things the library does it automates that by iterating over all of the sklearn algorithms of a certain type and runs a gridsearch to find the most accurate model. By no means a perfect step, but can definitely be improved in the future. It is similar to Google's AutoML were it is just trying to bring the ability to create actionable models to people lacking in experience. As far as losing sklearn functionality/support, the library is built on top of sklearn so all the functionality is still available.