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I have a multiclass-classifier whose macro-precision is always greater than macro-recall. I suppose it means false negatives outnumber false positives in general. Is there an intuitive interpretation about this?

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Generally lower recall means that the system is too strict, i.e. it predicts an instance as positive only when it has clear indications in the features that it's indeed a positive. As a consequence, it misses the true positive instances for which the indications are not so clear.

But when looking at macro-recall it's more complex: it depends primarily on how many classes there are and their distribution. In case of imbalance a very common problem is that the system predicts the majority class too often, causing a low recall for the minority class(es) and therefore a lower macro-recall. The only way to check for this is to look at the performance of individual classes.

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  • $\begingroup$ oh this is very intuitive. Thank you! $\endgroup$ Oct 12 '20 at 2:35

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