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Recall measures the proportion of the total possible true positive results that the model was able to identify.


Recall =                         true positives                   

                       true positives + false negatives


For example, for every 100 verbatims which should have been labelled as ‘Request for information’, the recall would be the percentage that the platform successfully found.


A 77% recall would mean that for every 100 verbatims that should have had a specific label predicted, there would be 23 verbatims which should have been predicted as having the label, but the platform missed them.


For a more detailed explanation on how recall works, please see here.

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