I'm going to make a big claim:
Libraries like sklearn (#python), caret (#rstats) and MLJ (#julialang)
are never going have great APIs for working with models like deep neural networks, or any other kind of model, where the hyper-parameters are nontrivial functions. And that is Ok
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Replying to @algo_luca
The kind of API you want for things like RandomForests and SVM has a fixed number of parameters and ways they can be changed. Once your parameters are functions then it is much harder to have full flexibility. But keeping it simple is good! Not everyone needs the full flexibility
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Replying to @oxinabox_frames
I agree and disagree at the same time. SVM and Random Forests are simpler model, and this made it easier to conflate different concepts (the model, the training algorithm, the configuration) in a single object. You can't get away with it once stuff gets more complex (see NN).
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At the same time, this restricts you when it comes to more advanced usage of those very same models (e.g. exoteric kernels for SVMs), as you just said.
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Writing about stuff to learn how it works, mostly in Rust.
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