An F-score is the harmonic mean of a system’s precision and recall values. It can be calculated by the following formula: 2 x [(Precision x Recall) / (Precision + Recall)]. Criticism around the use of F-score values to determine the quality of a predictive system is based on the fact that a moderately high F-score can be the result of an imbalance between precision and recall and, therefore, not tell the whole story. On the other hand, systems at a high level of accuracy struggle to improve precision or recall without negatively impacting the other.
Critical (risk) applications that value information retrieval more than accuracy (i.e., producing a large number of false positives but virtually guaranteeing that all the true positives are found) can adopt a different scoring system called F2 measure, where recall is weighed more heavily. The opposite (precision is weighed more heavily) is achieved by using the F0.5 measure.
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