= Precision and recall
For a <binary classifier>, <classification precision> is $TP/(TP+FP)$, the fraction of predicted positives that are correct, while <classification recall> is $TP/(TP+FN)$, the fraction of actual positives recovered. If a search returns ten items, six relevant, from a collection containing fifteen relevant items, precision is $6/10$ and recall is $6/15$. Raising a decision threshold often trades recall for precision, traced by a <precision-recall curve>, but precision need not improve at every threshold. Zero denominators require an explicit reporting convention. Both <measures> depend on which class is called positive; precision also depends strongly on prevalence.
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