Ranked Learning is a machine learning approach that focuses on ordering or ranking items based on their relevance or importance. This method is commonly used in applications like search engines and recommendation systems, where the goal is to present the most relevant results to users. By training models on labeled data, Ranked Learning helps improve the accuracy of predictions and recommendations. In Ranked Learning, algorithms evaluate various features of the items being ranked, such as user preferences or item characteristics. Techniques like Support Vector Machines and Gradient Boosting can be employed to optimize the ranking process. This approach enhances user experience by ensuring that the best options are prioritized.