Recall

Recall is TP / (TP + FN), the fraction of truly positive cases the model flags. The same quantity is called sensitivity in medicine, true positive rate (TPR) in ROC analysis and hit rate or detection rate in signal detection and security; its complement FN / (TP + FN) is the miss rate or false negative rate. Its counterpart for the negative class is specificity, TN / (TN + FP). Because recall is conditioned on the true class, it does not depend on prevalence the way precision does, but it does depend on the mix of positives: a detector evaluated only on easy, textbook attacks or clear-cut tumours will show a recall that will not hold on subtler cases, a problem known in diagnostics as spectrum bias.

Recall of 100% is trivially available by flagging everything, so it is only meaningful paired with precision or the false positive rate. Lowering the decision threshold can only keep recall the same or raise it. Requirements are therefore stated as an operating point, such as recall at a false positive rate of 1% or at a precision of at least 90%, and the threshold is chosen on the validation set. Averaging recall over classes gives balanced accuracy, and macro recall is a common headline metric for imbalanced multiclass tasks.

Measuring recall requires knowing every positive in the evaluation data, which is often the hard part. In a labelled test set it is given, but in large-scale retrieval the full set of relevant documents is unknown; TREC-style evaluations approximate it by pooling the top results of many systems, and technology-assisted review in e-discovery estimates recall by sampling the documents that were not flagged. In retrieval-augmented generation, retriever recall@k (whether the needed passage is among the top k chunks) caps end-to-end answer quality, since the generator cannot use evidence it never received. Summarisation metrics such as ROUGE are also recall-oriented, counting how much of the reference is covered.

Small positive counts make recall estimates wide. The example of 40 detected out of 50 test attacks gives 80%, but a Wilson 95% confidence interval runs from about 67% to 89%, so a second run with a different attack set could easily land ten points away. Red-team or penetration-test results used as recall estimates should therefore be reported with the number of attempts and, ideally, broken down by technique, since a detector's recall is rarely uniform across attack types.