False Negative Classification

AKA: FN Outcome.
Context:
- It can be a member of a False Negative Prediction Set (to calculate a false negative error rate)
Example:
- If a model predicts that a person does not have cancer when in fact they do have cancer then the prediction is labeled as a False Negative Prediction.
- a Type II Hypothesis Testing Error.
- …
Counter-Example(s):
- a True Negative Classification.
- a False Positive Classification.
See: Relation; Misclassification Error.

References

(Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/false_positives_and_false_negatives Retrieved:2015-1-20.
- In medical testing, and more generally in binary classification, a false positive is an error in data reporting in which a test result indicates that a condition – such as a disease – is present (the result is positive), but it is not in fact present (the result is false), while a false negative is when a test result indicates that a condition is not present (the result is negative), but it is in fact present (the result is true). These are the two kinds of errors in a binary test, and are contrasted with a correct result, either a Template:Visible anchor or a Template:Visible anchor. These are also known in medicine as a false positive diagnosis (resp. false negative diagnosis), and in statistical classification as a false positive error (resp. false negative error).
  In statistical hypothesis testing the analogous concepts are known as type I and type II errors, where a positive result corresponds to rejecting the null hypothesis, and a negative result corresponds to not rejecting the null hypothesis. The terms are often used interchangeably, but there are differences in detail and interpretation due to the differences between medical testing and statistical hypothesis testing.