Mean average precision (mAP) is a metric that assesses how well a system finds relevant results and ranks them near the top. Average precision is calculated across the result list for each query or object class; mAP is the mean across all of them. It is a standard measure for search engines, recommender systems and object detection in images, where it also depends on the required overlap of bounding boxes.
Recall (also called sensitivity or true positive rate) measures what share of the actual positive cases a model detects: correctly identified positives divided by all real positives. If a fraud filter catches 80 out of 100 real fraud cases, its recall is 0.8. High recall matters when missed cases are costly, such as in medical diagnosis. Because it ignores false alarms, it is usually considered together with precision, for example in the F-score.
The F-score is a metric for evaluating classification models. The common F1 score is the harmonic mean of precision (how many of the predicted hits are correct) and recall (how many of the actual hits were found), and ranges from 0 to 1. It is especially useful with imbalanced data: a spam filter that almost never flags spam can have high accuracy but a low F1 score.
Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.
Top-k accuracy measures how often the correct answer is among a model's k most likely suggestions. With top-1 only the first prediction counts; with top-5 it is enough for the correct class to be among the five best, a well-known metric from image recognition benchmarks such as ImageNet. It is useful wherever users see several suggestions, for example product recommendations or autocomplete.
