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PMML is fairly verbose and limited to a particular set of models. It's often easier to pickle the models and then keep tagged versions. I think a human readable format could be created, but since most models are just a pile of numbers it's unclear what is gained.

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For Logistic Regression we find human readable config makes a lot of sense. It's pretty intuitive if there aren't too many features - if the model starts behaving weirdly, we can sometimes track it down to a change in a single feature using this (especially when viewing recent git diffs).

Sure. I tend to keep my postprocessing of a model under version control. In particular, what features were most helpful for predictions.

Can't really talk about features on here :(

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