Making Sense of Data Features - DataScienceCentral.com

Spend any time at all in the machine learning space, and pretty soon you will encounter the term "feature". It's a term that may seem self-evident at first, but it very quickly descends into a level of murkiness that can leave most laypeople (and even many programmers) confused, especially when you hear examples of machine learning systems that involve millions or even billions of features. If you take a look at a spreadsheet, you can think of a feature as being roughly analogous to a column of data, along with the metadata that describes that column. This means that each cell in that column (which corresponds to a given "record") becomes one item in an array, not including any header labels for that column. The feature could have potentially thousands of values, but they are all values of the same type and semantics.