I have performed classification using multiple classifiers for a 2-classes labelled data, and I used 5-fold cross validation. For each fold I calculated tp, tn, fp, and fn. Then I calculated the accuracy, precision, recall and F-score for each test. My question is, when I want to average the results, I took the average of accuracies, but can I average precision, recall and the F-score as well? Or would this be mathematically wrong? P.S. The datasets used in each fold are well balanced in terms of the number of instances per class.


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    $\begingroup$ I encountered your same problem regarding computing the F-measure (harmonic mean of precision and recall) using cross-validation. In this paper they actually demonstrated that computing the F-measure on the complete set, and not averaging, is the less biased method. I hope this can help $\endgroup$
    – papafe
    Oct 13, 2014 at 14:32
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    $\begingroup$ @markusian Please add this as an answer! It is by far the most important thing on this page!! $\endgroup$
    – drevicko
    Feb 3, 2016 at 19:03

1 Answer 1


The $F$-score, assuming you're using the usual definition, is already a combination of the precision and recall. Specifically, it is the harmonic mean of them. In other words $$F_1 = 2\cdot\frac{\textrm{precision} \cdot \textrm{recall}}{\textrm{precision} + \textrm{recall}}$$ It's meant to capture the 'effectiveness' of a system where the user places equal weights on precision and recall. There's an extension, called the $F_\beta$ score, which gives $\beta$ times more weight to recall than precision. $$ F_\beta = (1+\beta^2) \frac{\textrm{precision} \cdot \textrm{recall}}{(\beta^2 \cdot\textrm{precision}) + \textrm{recall}} $$ On the other hand, if you're asking whether you can average the 5 $F$ scores (one from each fold), then the answer is yes. In fact, that's the typical way to report a system's performance!

Just be aware that there are some issues with using these values to make inferences about the classifiers' generalization error. For example, a $t$-test between the $F$ scores for one classifier and the $F$ scores for another classifier is going to be too optimistic.

  • $\begingroup$ Yes, I used the first formula. This means that averaging the F-score from the different tests yields similar results to averaging precision and recall and then calculating the F-score from them. I tried this on the results I have and it was almost the same. Thanks. $\endgroup$
    – Kalaji
    Aug 8, 2013 at 17:57

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