Editing Statistical classification (section)

{{Short description|Categorization of data using statistics}}
When [[classification]] is performed by a computer, statistical methods are normally used to develop the algorithm.

Often, the individual observations are analyzed into a set of quantifiable properties, known variously as [[explanatory variables]] or ''features''.  These properties may variously be [[categorical data|categorical]] (e.g. "A", "B", "AB" or "O", for [[blood type]]), [[ordinal data|ordinal]] (e.g. "large", "medium" or "small"), [[integer|integer-valued]] (e.g. the number of occurrences of a particular word in an [[email]]) or [[real number|real-valued]] (e.g. a measurement of [[blood pressure]]). Other classifiers work by comparing observations to previous observations by means of a [[similarity function|similarity]] or [[metric (mathematics)|distance]] function.

An [[algorithm]] that implements classification, especially in a concrete implementation, is known as a '''classifier'''.  The term "classifier" sometimes also refers to the mathematical [[function (mathematics)|function]], implemented by a classification algorithm, that maps input data to a category.

Terminology across fields is quite varied. In [[statistics]], where classification is often done with [[logistic regression]] or a similar procedure, the properties of observations are termed [[explanatory variable]]s (or [[independent variable]]s, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the [[dependent variable]].  In [[machine learning]], the observations are often known as ''instances'', the explanatory variables are termed ''features'' (grouped into a [[feature vector]]), and the possible categories to be predicted are ''classes''.  Other fields may use different terminology: e.g. in [[community ecology]], the term "classification" normally refers to [[cluster analysis]].