Editing Pattern recognition (section)

===Probabilistic classifiers===
{{Main|Probabilistic classifier}}
Many common pattern recognition algorithms are ''probabilistic'' in nature, in that they use [[statistical inference]] to find the best label for a given instance. Unlike other algorithms, which simply output a "best" label, often probabilistic algorithms also output a [[probability]] of the instance being described by the given label. In addition, many probabilistic algorithms output a list of the ''N''-best labels with associated probabilities, for some value of ''N'', instead of simply a single best label. When the number of possible labels is fairly small (e.g., in the case of [[classification (machine learning)|classification]]), ''N'' may be set so that the probability of all possible labels is output. Probabilistic algorithms have many advantages over non-probabilistic algorithms:
*They output a confidence value associated with their choice. (Note that some other algorithms may also output confidence values, but in general, only for probabilistic algorithms is this value mathematically grounded in [[probability theory]]. Non-probabilistic confidence values can in general not be given any specific meaning, and only used to compare against other confidence values output by the same algorithm.)
*Correspondingly, they can ''abstain'' when the confidence of choosing any particular output is too low.
*Because of the probabilities output, probabilistic pattern-recognition algorithms can be more effectively incorporated into larger machine-learning tasks, in a way that partially or completely avoids the problem of ''error propagation''.