Feature (pattern Recognition)
In machine learning and pattern recognition, a feature is an individual measurable heuristic property of a phenomenon being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition.
The set of features of a given data instance is often grouped into a feature vector. The reason for doing this is that the vector can be treated mathematically. For example, many algorithms compute a score for classifying an instance into a particular category by linearly combining a feature vector with a vector of weights, using a linear predictor function.
The concept of "feature" is essentially the same as the concept of explanatory variable used in statistical techniques such as linear regression.
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