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This is very similar to fitting a linear regression and not including an intercept, and I think they will face similar issues. To be very concrete, consider an example with $f(x)=1,\ E=1, \ \phi_1=1, \ \phi_2=-1$. Then your scaling factor is undefined, trying to divide by zero. Well OK, but you won't often get such exact numbers. Let's tweak them to $$f(x)=...


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In short: no, one cannot feed a ML system with massive random heterogeneous data and expect the system to make sense of it by itself. ML is not magical, it needs to be fed with the right information in order to produce a meaningful and reliable answer. The closest application to this idea is Question Answering (QA). QA is an NLP task where the system answers ...


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I think the question was asked to see how would you approach the problem. In similar questions, there is not a single answer, and the interviewer does not expect a certain answer instead expects a reasonable approach by you. It is like the famous interview question "How many golf balls can you fit into a swimming pool?". Such a question is asked to ...


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