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I find it difficult to believe anyone who can use the models listed on the repository effectively would have any difficulty using scikit themselves.

Abstracting scikit out into a configuration file only very slightly simplifies the actual code involved but I can see this being useful for some non technical users who don't care about the code and just know the ML terms.

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It's not about that someone will have difficulty using sklearn. It's more about how clean the approach is if you have all your configs in a yaml file and you can change things very easily/quickly and rerun an experiment. I'm working with data & ML models everyday and it became overwhelming when my codebase is large and I want to change small things and re-run an experiment. Also It would be great to not lose much time writing that code in the first place (although it's easy to do), if you want a quick and dirty draft. The thing is, it is much cleaner if you have your preprocessing methods and model definition in one file. However, there are other features that will be integrated soon, like a simple gui built in python

This is a good point, something that I've been struggling with in my own personal projects is keeping track of parameters as I tweak and play with hyper-parameters and model structures.

A few parameters are fine, you can pull them out into constants, but you quickly end up with a lot of variables to keep track of.

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