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0 pointssrean7y ago0 comments

Machine vision is actually a good place to be in this context. There is a lot of institutional knowledge on what good features are, and if you have enough data DNNs can do a lot of heavy lifting. If you arent doing DNNs, the decision on how you featurize the problem will probably play the most significant role.

Its some of the other areas of ML where its not known ahead of time what the good features would be that tooling for feature engineering would help a great deal. Especially when one doesn't have the luxury of throwing a DNN at it and be done with it. The situation has both good and bad parts, the good part is that there is a lot of space to be creative in the design of features.

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mcrad7y ago· 1 in thread

Cool, thanks. I suppose financial/risk modeling is also primarily "feature engineering"; I've just never seen it as separate from the modeling itself. Perhaps it's because I've typically been as involved in the domain expertise as in the math part.

sreanOP7y ago

Feature engineering is about going from a concrete real world problem to a representation that a computer can operate on. Furthermore, this representation has to facilitate solving the problem.

DNNs are good at trying many alternative representations on their own, but you need a lot of data. When you cannot use these beasts, one has to be creative and think of good ways to represent the problem (either as numbers or syntactic structures such as graphs).

Once the representation part is done then one can throw computation and maths at the problem.

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