👉 Recall computing is a fundamental concept in information retrieval and machine learning, focusing on measuring a system's ability to retrieve all relevant instances from a dataset. It is defined as the ratio of correctly retrieved relevant items to the total number of relevant items available in the dataset. Mathematically, it's expressed as Recall = C / (C + F), where C is the number of relevant items retrieved and F is the total number of relevant items in the dataset. High recall indicates that the system effectively captures most of the relevant information, minimizing the loss of important data. However, it's often balanced against precision, as overly aggressive retrieval can lead to a high number of irrelevant results, reducing overall system effectiveness.
