False Negative Error Rate
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A False Negative Error Rate is a classification performance measure that is based on the probability that a predictive relation will make the incorrect prediction of mapping a false test instance to a negative prediction.
- AKA: FNR, Type 2 Error Rate.
- Context:
- Example(s):
- The likelihood that a patient with cancer will be told that they do not have it.
- …
- Counter-Example(s):
- See: Confusion Matrix.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/False_positives_and_false_negatives#false_negative_rate Retrieved:2015-7-19.
- Complementarily, the Template:Visible anchor is the proportion of events that are being tested for which yield negative test outcomes with the test, i.e., the conditional probability of a negative test result given that the event being looked for has taken place.
In statistical hypothesis testing, this fraction is given the letter β. The “power” (or the “sensitivity") of the test is equal to 1−β.
- Complementarily, the Template:Visible anchor is the proportion of events that are being tested for which yield negative test outcomes with the test, i.e., the conditional probability of a negative test result given that the event being looked for has taken place.