![]() However, the F-measures do not take true negatives into account, hence measures such as the Matthews correlation coefficient, Informedness or Cohen's kappa may be preferred to assess the performance of a binary classifier. The F-score is also used in machine learning. The more generic F β is seen in wide application. It thus symmetrically represents both precision and recall in one metric. The F 1 score is the harmonic mean of the precision and recall. Precision is also known as positive predictive value, and recall is also known as sensitivity in diagnostic binary classification. ![]() 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 positive results, including those not identified correctly, and the recall is the number of true positive results divided by the number of all samples that should have been identified as positive. In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance.
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