Recall refers to the ability of a machine learning model to identify relevant instances of a particular class or category accurately. More specifically, it is a metric that measures the proportion of actual positive instances identified by the model as positive out of all the positive instances in the dataset.
Recall is also referred to as sensitivity or true positive rate. It is a crucial evaluation metric for classification models, especially when identifying all positive instances is critical, for example, in medical diagnosis or fraud detection.
Let us take an example: In a binary classification problem where the goal is to predict whether an email is spam or not, recall helps to measure the proportion of all spam emails that are correctly classified as spam by the model out of all the spam emails in the dataset. A high recall value indicates that the model is good at identifying all spam emails, while a low recall value suggests that the model is missing some spam emails.
Applications of Recall in AI
- Recall is important in medical diagnosis, where identifying positive instances (such as tumors or diseases) is critical. A high recall value allows accurate diagnoses and better patient outcomes.
- In fraud detection, too, recall plays a vital role. A high recall value can help prevent financial losses and improve security.
- Recall is a crucial metric in information retrieval systems, where the goal is to retrieve all relevant documents related to a particular query or topic. A high recall value, in this case, allows for accurate and comprehensive search results.
- In object detection tasks, where the target is to identify all instances of a specific object or class in an image or video, recall plays an important role. A high recall value allows for more accurate and complete object detection.
