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Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]
By this definition, ε equals the value of the unit in the last place relative to 1, i.e. () (where b is the base of the floating point system and p is the precision) and the unit roundoff is u = ε / 2, assuming round-to-nearest mode, and u = ε, assuming round-by-chop.
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).
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:
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 ...
Provided the data are strictly positive, a better measure of relative accuracy can be obtained based on the log of the accuracy ratio: log(F t / A t) This measure is easier to analyze statistically and has valuable symmetry and unbiasedness properties.
In information retrieval, the positive predictive value is called precision, and sensitivity is called recall. Unlike the Specificity vs Sensitivity tradeoff, these measures are both independent of the number of true negatives, which is generally unknown and much larger than the actual numbers of relevant and retrieved documents.
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.