Editing Machine learning (section)

=== Rule-based models ===
{{Main|Rule-based machine learning}}
Rule-based machine learning (RBML) is a branch of machine learning that automatically discovers and learns 'rules' from data. It provides interpretable models, making it useful for decision-making in fields like healthcare, fraud detection, and cybersecurity. Key RBML techniques includes [[learning classifier system]]s,<ref>{{Cite journal |last1=Urbanowicz |first1=Ryan J. |last2=Moore |first2=Jason H. |date=22 September 2009 |title=Learning Classifier Systems: A Complete Introduction, Review, and Roadmap |journal=Journal of Artificial Evolution and Applications |language=en |volume=2009 |pages=1–25 |doi=10.1155/2009/736398 |issn=1687-6229 |doi-access=free }}</ref> [[association rule learning]],<ref>Zhang, C. and Zhang, S., 2002. ''[https://books.google.com/books?id=VqSoCAAAQBAJ Association rule mining: models and algorithms]''. Springer-Verlag.</ref> [[artificial immune system]]s,<ref>De Castro, Leandro Nunes, and Jonathan Timmis. ''[https://books.google.com/books?id=aMFP7p8DtaQC&q=%22rule-based%22 Artificial immune systems: a new computational intelligence approach]''. Springer Science & Business Media, 2002.</ref> and other similar models. These methods extract patterns from data and evolve rules over time.