metrics recall

Recall is a metric used in machine learning and information retrieval to evaluate the performance of a model or system in identifying relevant items. Specifically, recall measures the proportion of relevant items that were correctly identified by the system out of all the relevant items in the dataset.

Mathematically, recall can be defined as:

Recall = True Positives / (True Positives + False Negatives)

where True Positives are the correctly identified relevant items and False Negatives are the relevant items that were not identified by the system.

In other words, recall is a measure of how well a system can identify all relevant items in the dataset, regardless of how many irrelevant items it may also identify. A high recall means that the system is good at identifying relevant items, even if it sometimes mistakenly identifies some irrelevant items as relevant. A low recall means that the system is missing some relevant items and may be biased towards identifying only the most obvious relevant items.