# Binary Classification Performance Measure

A Binary Classification Performance Measure is a classification performance measure for a binary classification task.

**Context:**- …

**Example(s):**- for Boolean Classification Tasks,
- False Positive Error Rate: FP / (TN + FP).
- False Discovery Rate: FP / (TP + FN).
- False Negative Error Rate: FP / (TP + FP).
- True Positive Success Rate: TP / (TP + FN).
- True Negative Rate: TN / (TN + FP).

- for Confidence Score Producing Tasks:

- for Boolean Classification Tasks,
**Counter-Example(s):****See:**Accuracy, Predictive Relation, Confusion Matrix, Statistical Test.

## References

### 2009

- Eric W. Weisstein. "Statistical Test." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/StatisticalTest.html
- QUOTE: A test used to determine the statistical significance of an observation. Two main types of error can occur:
- 1. A type I error occurs when a false negative result is obtained in terms of the null hypothesis by obtaining a false positive measurement.
- 2. A type II error occurs when a false positive result is obtained in terms of the null hypothesis by obtaining a false negative measurement.

- The probability that a statistical test will be positive for a true statistic is sometimes called the test's sensitivity, and the probability that a test will be negative for a negative statistic is sometimes called the specificity. The following table summarizes the names given to the various combinations of the actual state of affairs and observed test results.
- result name
- true positive result sensitivity
- false negative result 1-sensitivity
- true negative result specificity
- false positive result 1-specificity

- QUOTE: A test used to determine the statistical significance of an observation. Two main types of error can occur: