For in calculating success in information retrieval, precision and recall are fairly standard measurements, relating to accuracy of the results, and to what extent the results are comprehensive, respectively.However, recall values typically require that you know how many correct results there are in total (in order to be able to state to what extent these results have been returned). This is, of course, problematic, if you do not know how many correct results there are in the first place.But supposing you have x number of results. You do not know how they relate to the absolute number of correct documents that could have been returned, but you do know how accurate these results are. You can increase this accuracy, but you will lose some correct results in the process. You could exclude every result from x bar one, and ensure you have 100% accuracy, but clearly there has been a dramatic decrease in relative recall through this action. But what is the technically correct term to be associated with this relative recall, when you do not have any idea what the absolute value of the recall in this environment is? Is there such a term?EXAMPLEFor instance supposing you were developing an IR system to return articles relating to the Call of Duty video game franchise and it gives you the following resultshttp://www.theverge.com/2016/5/2/11564464/call-of-duty-infinite-warfare-release-date-first-trailerhttps://www.theguardian.com/technology/2016/may/03/call-of-duty-infinite-warfarehttp://www.asianage.com/editorial/beyond-call-duty-943http://europe.newsweek.com/chinas-military-attempts-attract-millennials-call-duty-inspired-recruitment-455425?rm=euhttp://www.breitbart.com/video/2016/05/16/call-duty-black-ops-iii-eclipse-multiplayer-trailer/http://www.journalgazette.net/news/local/courts/County-residents-getting-bogus-jury-duty-calls-13146090http://www.straitstimes.com/singapore/health/healthcare-workers-who-went-beyond-call-of-dutyNow you don't know what the absolute number of correct articles are for the call of duty franchise: but you do know the number in the dataset your IR system has produced. Above represents a precision of 42.85% and a relative recall of 100%. A cut list of http://www.theverge.com/2016/5/2/11564464/call-of-duty-infinite-warfare-release-date-first-trailerhttps://www.theguardian.com/technology/2016/may/03/call-of-duty-infinite-warfarehttp://www.asianage.com/editorial/beyond-call-duty-943Has precision of 66.66%, but at the expense of one of the correct articles, thereby affecting recall. So what is the correct term for this so-called recall?
Term for relative recall
information retrieval;terminology;confusion matrix
In the x results obtained from the IR system, let TP, FP, FN, TN be the True Positives, False Positives, False Negatives and True Negatives of the trial, respectively.When you take a random smaller-sized sample s of x observations (cut list in your language), the values TP and FP decrease (by p and q) in their respective ratios. This is compensated by proportional increase (by p and q) in values FN and TN, conserving the actual number of binary classes.TP + FN = (TP - p) + (FN + p)FP + TN = (FP - q) + (TN + q)x = TP + FP, s = (TP - p) + (FP - q)For a good classifier, TP >>> FP. So decrease in already minimal FP (in new sample), results in improved precision. Now your concept of relative recall comes into picture, which in this case can be expressed as(TP - p)/(TP+FN + p)where TP acts as actual true observations (ground-truth) for the new sample.Note: FN is not known because ground-truth labels are not available.The decrease in recall is caused by type-2 error, which is the FN rate (Not reporting a link as Call of Duty when it actually is), also denoted  (beta). So this reduction in recall (relative recall) is attributed to rise in False Negative Rate (miss rate), sinceRecall = 1 - FN Rate