Feature Vectors
Most algorithms describe an individual instance whose category is to be predicted using a feature vector of individual, measurable properties of the instance. Each property is termed a feature, also known in statistics as an explanatory variable (or independent variable, although in general different features may or may not be statistically independent). Features may variously be binary ("male" or "female"); categorical (e.g. "A", "B", "AB" or "O", for blood type); ordinal (e.g. "large", "medium" or "small"); integer-valued (e.g. the number of occurrences of a particular word in an email); or real-valued (e.g. a measurement of blood pressure). If the instance is an image, the feature values might correspond to the pixels of an image; if the instance is a piece of text, the feature values might be occurrence frequencies of different words. Some algorithms work only in terms of discrete data and require that real-valued or integer-valued data be discretized into groups (e.g. less than 5, between 5 and 10, or greater than 10).
The vector space associated with these vectors is often called the feature space. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction techniques can be employed.
Read more about this topic: Classification (machine Learning)
Famous quotes containing the word feature:
“The paid wealth which hundreds in the community acquire in trade, or by the incessant expansions of our population and arts, enchants the eyes of all the rest; the luck of one is the hope of thousands, and the bribe acts like the neighborhood of a gold mine to impoverish the farm, the school, the church, the house, and the very body and feature of man.”
—Ralph Waldo Emerson (1803–1882)