Recall Metric
From GMRKB
A Recall Metric is a performance metric for a binary classification model that is based on the Probability that a true test instance is a positive prediction
 AKA: Recall, True Positive Rate, Sensitivity, R, TPR.
 Context:
 It can be Estimated by: TP / (TP + FN)

# of correct answers given by the system as a proportion of the total # of possible correct predictions
)  the proportion of cases with a positive test result who are correctly diagnosed.

 It can be illustrated over a series of cutoffs for defining an Accurate Prediction with a Receiver Operator Curve
 It can be Estimated by: TP / (TP + FN)
 Example(s):
 The probability of a positive test result in a patient who has the disease under consideration. E.g. probability that a test for cancer will predict that a patient has cancer when in fact they do have cancer.
 CounterExample(s):
 See: Receiver Operator Curve, Prevalence.
References
2011
 (Sammut & Webb, 2011) ⇒ Claude Sammut, and Geoffrey I. Webb. (2011). "Recall." In: (Sammut & Webb, 2011) p.829
 (Sammut & Webb, 2011) ⇒ Claude Sammut, and Geoffrey I. Webb. (2011). "Sensitivity." In: (Sammut & Webb, 2011) p.901
2009
 (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Sensitivity_(tests)#Sensitivity
 A sensitivity of 100% means that the test recognizes all sick people as such. Thus in a high sensitivity test, a negative result is used to rule out the disease.
 Sensitivity alone does not tell us how well the test predicts other classes (that is, about the negative cases). In the binary classification, as illustrated above, this is the corresponding specificity test, or equivalently, the sensitivity for the other classes.
 http://www.health.state.mn.us/newbornscreening/glossary.html#positive
 Positive predictive value  This is a measure of how well a test correctly finds individuals who truly have the condition being checked for. It is the proportion of individuals with positive test results who are correctly diagnosed.
2000
 2000_SpeechAndLanguageProcessing
 "Recall is a measure of how much relevant information the system has extracted from the text; it is thus a measure of the coverage of the system."
1998
 (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). "Glossary of Terms." In: Machine Leanring 30(23).
 True positive rate (Recall, Sensitivity): d/(c+d).
 Sensitivity: True positive rate (see Confusion matrix).