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LazyEval
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Is it good practice to include data cleaning or feature engineering steps in an sklearn pipeline to create a scalable pipeline?
So you would make custom transformers for all transformations to be applied to the data? What do you mean scikit-learn can handle production data? When I refer to the whole dataset, I mean before splitting into training/valid/test sets. Couldn't transformations on data that do not cause data leakage be applied on the entire dataset, i.e. before splitting?
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Does standardization result in normal distribution?
I'm not sure to be honest. Can you share a couple of those links?
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