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It is an important part of an observation for learning about the structure of the problem that is being modeled. If a feature has no impact on the problem, it is not part of the problem.
With good features, you are closer to the underlying problem and a representation of all the data you have available and could use to best characterize that underlying problem. — Tomasz Malisiewicz, answer to “What is feature engineering?
Each could be an in depth article of their own as they are large and important areas of practice and study. Tabular data is described in terms of observations or instances (rows) that are made up of variables or attributes (columns). The idea of a feature, separate from an attribute, makes more sense in the context of a problem.
A feature is an attribute that is useful or meaningful to your problem. I think there is no such thing as a non-meaningful feature.
I would think to myself “” The goal was right, but the approach was one of many.
In this section we look at these many approaches and the specific sub-problems that they are intended to address.