Definition
Recall refers to the ability of an AI system to accurately retrieve relevant information from its training data or a knowledge base when prompted. It is a measure of how well the AI can bring back all the correct items related to a query, ensuring completeness of information. In practice, recall is crucial for AI applications that need to identify all instances of a particular entity or event, such as in medical diagnosis or fraud detection. High recall means the system is less likely to miss important information, which is vital for making informed decisions and avoiding critical errors. For example, a search engine with high recall would return all documents relevant to a user's query, not just a few. This differs from precision, which measures the proportion of retrieved items that are actually relevant, meaning high recall can sometimes come with lower precision. Understanding recall is important for AI and machine learning certifications, particularly in domains focused on data analysis and information retrieval.