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- Q2359161 subject Q6491658.
- Q2359161 subject Q7217021.
- Q2359161 subject Q8825053.
- Q2359161 abstract "In pattern recognition and information retrieval with binary classification, precision (also called positive predictive value) is the fraction of retrieved instances that are relevant, while recall (also known as sensitivity) is the fraction of relevant instances that are retrieved. Both precision and recall are therefore based on an understanding and measure of relevance. Suppose a computer program for recognizing dogs in scenes from a video identifies 7 dogs in a scene containing 9 dogs and some cats. If 4 of the identifications are correct, but 3 are actually cats, the program's precision is 4/7 while its recall is 4/9. When a search engine returns 30 pages only 20 of which were relevant while failing to return 40 additional relevant pages, its precision is 20/30 = 2/3 while its recall is 20/60 = 1/3.So, in this case, precision is 'how useful the search results are', and recall is 'how complete the results are'.In statistics, if the null hypothesis is that all and only the relevant items are retrieved, absence of type I and type II errors corresponds respectively to maximum precision (no false positive) and maximum recall (no false negative). The above pattern recognition example contained 7 − 4 = 3 type I errors and 9 − 4 = 5 type II errors. Precision can be seen as a measure of exactness or quality, whereas recall is a measure of completeness or quantity.In simple terms, high precision means that an algorithm returned substantially more relevant results than irrelevant, while high recall means that an algorithm returned most of the relevant results.".
- Q2359161 thumbnail Precisionrecall.svg?width=300.
- Q2359161 wikiPageExternalLink Preface.html.
- Q2359161 wikiPageExternalLink summary?doi=10.1.1.27.4637.
- Q2359161 wikiPageExternalLink computing-precision-and-recall-for.html.
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- Q2359161 wikiPageWikiLink Q2359161.
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- Q2359161 wikiPageWikiLink Q6491658.
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- Q2359161 wikiPageWikiLink Q7217021.
- Q2359161 wikiPageWikiLink Q7882493.
- Q2359161 wikiPageWikiLink Q816826.
- Q2359161 wikiPageWikiLink Q8825053.
- Q2359161 wikiPageWikiLink Q989120.
- Q2359161 comment "In pattern recognition and information retrieval with binary classification, precision (also called positive predictive value) is the fraction of retrieved instances that are relevant, while recall (also known as sensitivity) is the fraction of relevant instances that are retrieved. Both precision and recall are therefore based on an understanding and measure of relevance.".
- Q2359161 label "Precision and recall".
- Q2359161 depiction Precisionrecall.svg.