7.00.02 - Background - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)

In statistical analysis of binary classification, the F1 score (or F-score or F-measure) is a measure of a test’s accuracy that is based on both precision and recall, which are defined as follows:

  • Precision, p, is the number of observations that are correctly classified as positive divided by the number of observations that are classified as positive.
  • Recall, r, is the number of observations that are correctly classified as positive divided by the number of observations that are positive.

The F1 score can be interpreted as a weighted average of precision and recall, whose best value is 1 and worst value is 0.

The traditional F1 score is the harmonic mean of precision and recall:

F =2*p*r / (p+r)

The FMeasure function is not restricted to binary classification.

The general formula for a positive real β is:

Fβ =(1+β*β)*p*r /(β*β*p+r)