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:
    • Log Loss.
  • …
• Counter-Example(s):
  • an n-Ary Classification Performance Measure.
• See: Accuracy, Predictive Relation, Confusion Matrix, Statistical Test.

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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

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