Recall Metric

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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



References

2011

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