= Feature
{disambiguate=machine learning}
A <feature (machine learning)> is an input attribute or computed representation used by a <machine learning> system. An example can be represented by a <vector> whose coordinates are individual <feature (machine learning)> values; a <design matrix> commonly places examples in rows and these coordinates in columns. A feature can be measured directly or derived from other measurements.<Deep Learning by Ian Goodfellow and others>[§§5.1.1 and5.1.3, example vectors, features and design matrices]{ref}
For example, classifying manufactured parts might use measured length and width as two <feature (machine learning)> inputs. Their ratio is another possible <feature (machine learning)>, exposing a shape relationship to a simple model. Choosing a representation changes which rules are easy to learn. A variable available only after the desired prediction time can introduce <data leakage>, while a <distributed representation> can learn useful coordinates without assigning each one a human-readable meaning.
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