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What is the advantage of using the harmonic mean rather than averaging using the arithmetic mean?
Since the harmonic mean of a list of numbers tends strongly toward the least elements of the list, it tends (compared to the arithmetic mean) to mitigate the impact of large outliers and aggravate the impact of small ones. The arithmetic mean is often mistakenly used in places calling for the harmonic mean.
Is harmonic mean affected by extreme values?
It is not affected by extreme values.
How do you interpret an f-score?
The F1 score can be interpreted as a weighted average of the precision and recall values, where an F1 score reaches its best value at 1 and worst value at 0. See Analyzing low F1 scores.
Which is the harmonic mean of the F1 score?
The F1 score is the harmonic mean of the precision and recall.
Which is the best value for the F1 score?
The F1 score does this by calculating their harmonic mean, i.e. F1 := 2 / (1/precision + 1/recall). It reaches its optimum 1 only if precision and recall are both at 100%. And if one of them equals 0, then also F1 score has its worst value 0.
How does the F1 score relate to precision?
F1 score gives the same weightage to recall and precision. There is a weighted F1 score in which we can give different weightage to recall and precision. As discussed in the previous section, different problems give different weightage to recall and precision. Beta represents how many times recall is more important than precision.
Which is the harmonic mean of precision and recall?
It is the harmonic mean of precision and recall. It takes both false positive and false negatives into account. Therefore, it performs well on an imbalanced dataset. F1 score gives the same weightage to recall and precision. There is a weighted F1 score in which we can give different weightage to recall and precision.