Rule-based machine learning

Rule-based machine learning refers to a class of algorithmic approaches that utilize rules to make decisions or predictions based on input data. These rules are usually derived from the data itself, expert knowledge, or a combination of both. Rule-based systems can be particularly useful in situations where interpretability and transparency are important, as the rules provide a clear, understandable way of representing the logic behind the decisions made by the system.