Rule-Based Classification
Rule-based classification is a machine learning and data mining technique that uses a set of predefined rules to categorize data into classes. These rules are typically expressed as 'if-then' statements, where conditions on input features determine the output class. It is a transparent and interpretable approach, often used in expert systems and scenarios where decision logic needs to be explicitly defined.
Developers should learn rule-based classification when building systems that require high interpretability, such as in healthcare, finance, or legal applications where decisions must be explainable. It is also useful for prototyping or when labeled data is scarce, as rules can be manually crafted based on domain knowledge. However, it may not scale well for complex, high-dimensional data compared to statistical or deep learning methods.