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Is there more to modeling than transforming the data, fitting the model, and improving the accuracy?
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I’m relatively new to machine learning, but it currently seems like the process is rather formulaic: I perform feature engineering, train a model, and train hyperparams/score it, repeating parts of this process.
Is this all that machine learning is?
Edit: Thank you all for the input, I see that being reductionist isn’t helpful!
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