p.s. very important: I’m talking about numbers that don’t transform the original data set in any way.
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black box ML is a very difficult problem. One size fits all is impossible, you need to constrain the problem upstream.
Things you need to take into account: 1) number of samples available 2) type of problem 3) class imbalance 4) overfitting ...etc
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here's an example of a (still very imperfect) system that attempts to do black box ML: https://github.com/rhiever/tpot
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ok, basically this handles the ML parameters as an optimization problem using genetic algos.
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