350px|Precision and recall In pattern recognition, information retrieval and classification (machine learning), precision and recall are criteria that apply to individual data instances retrieved from a collection, corpus or sample space. precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance. Suppose a computer program for recognizing dogs (the relevant element) in photographs identifies eight dogs in a picture containing ten cats and twelve dogs, and of the eight it identifies as dogs, five actually are dogs (true positives), while the other three are cats (false positives). Seven dogs were missed (false negatives), and seven cats were correctly excluded (true negatives). The program's precision is then 5/8 (true positives / selected elements) while its recall is 5/12 (true positives / all positives).
350px|Precision and recall In pattern recognition, information retrieval and classification (machine learning), precision and recall are criteria that apply to individual data instances retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digitized collection of photographs. Upon running a query, the program identifies eight dogs in a picture containing ten cats and twelve dogs, and of the eight it identifies as dogs, five actually are dogs (true positives), while the other three are cats (false positives). Seven dogs were missed (false negatives), and seven cats were correctly excluded (true negatives). The program's precision is then 5/8 (true positives / selected elements) while its recall is 5/12 (true positives / all positives).
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a digitized collection of photographs. Upon running a query, the program