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F-measure is considered as the harmonic mean (or weighted average) of precision and recall.
F-Measure: The weighted average of precision and recall of classifications (values from 0 to 1).
The F-measure is the harmonic average of precision and recall, and a balanced F-measure is expressed as: F 1 = 2pr/(p+r).
Note that the F measure is the harmonic average of precision and recall, which is why the change in F measure is not exactly the difference between the change in precision and recall.
These techniques fail to estimate statistical uncertainty for individual estimates, and provide only an overall average of precision.
In Figure 11, for the average of Precision, FOA-SVM is the best, which is 100%.
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The mean average precision (mAP) [13] over tags are found by computing for each tag the average of precisions measured after each relevant image is retrieved.
The same conclusions can be deduced from the resulting average values of precision and recall in Table 2.
For a given ranking, the average precision is the average of all precisions computed at ranks containing relevant documents.
The F-score can be interpreted as a weighted average of the precision and recall.
The best -measure, defined as a harmonic average of the precision and recall rate, becomes 0.74.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com