Rule Based Model

Rule-based models offer a powerful approach to machine learning by representing predictions as sets of explicit, human-interpretable rules. Current research focuses on improving their accuracy and scalability, particularly through hybrid approaches combining rule-based systems with deep learning models like transformers and neural networks, and on addressing challenges like handling missing data and enhancing explainability for complex models. This work is significant because interpretable models are crucial in high-stakes domains such as healthcare and finance, where understanding model decisions is paramount, and also provide valuable insights into the workings of "black box" models.