precision

In pattern recognition, information retrieval and classification (machine learning), 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 the total amount of relevant instances that were actually retrieved. Both precision and recall are therefore based on an understanding and measure of relevance.
Suppose a computer program for recognizing dogs in photographs identifies 8 dogs in a picture containing 12 dogs and some cats. Of the 8 identified as dogs, 5 actually are dogs (true positives), while the rest are cats (false positives). The program's precision is 5/8 while its recall is 5/12. 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 items are irrelevant (where the hypothesis is accepted or rejected based on the number selected compared with the sample size), absence of type I and type II errors (i.e. perfect sensitivity and specificity of 100% each) corresponds respectively to perfect precision (no false positive) and perfect recall (no false negative). The above pattern recognition example contained 8 − 5 = 3 type I errors and 12 − 5 = 7 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 returns substantially more relevant results than irrelevant ones, while high recall means that an algorithm returns most of the relevant results (whether or not irrelevant ones are also returned).
The relationship between sensitivity and specificity to precision depends on the proportion of positive cases in the population, also known as prevalence; with fixed sensitivity and specificity, precision rises with increasing prevalence.

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