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In statistics, the precision matrix or concentration matrix is the matrix inverse of the covariance matrix or dispersion matrix, =. [ 1 ] [ 2 ] [ 3 ] For univariate distributions , the precision matrix degenerates into a scalar precision , defined as the reciprocal of the variance , p = 1 σ 2 {\displaystyle p={\frac {1}{\sigma ^{2}}}} .
The field of statistics, where the interpretation of measurements plays a central role, prefers to use the terms bias and variability instead of accuracy and precision: bias is the amount of inaccuracy and variability is the amount of imprecision.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
Precision and recall. In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly ...
Precision takes all retrieved documents into account. It can also be evaluated considering only the topmost results returned by the system using Precision@k. Note that the meaning and usage of "precision" in the field of information retrieval differs from the definition of accuracy and precision within other branches of science and statistics.
An F-score is a combination of the precision and the recall, providing a single score. There is a one-parameter family of statistics, with parameter β, which determines the relative weights of precision and recall. The traditional or balanced F-score is the harmonic mean of precision and recall:
Its formal use to refer to a specific function in mathematical statistics was proposed by Ronald Fisher, [43] in two research papers published in 1921 [44] and 1922. [45] The 1921 paper introduced what is today called a "likelihood interval"; the 1922 paper introduced the term " method of maximum likelihood ".