Meta-learning is a type of machine learning that helps us choose the best algorithms for solving specific problems by analyzing data from other machine learning models. This research paper discusses recent advancements in meta-learning for supervised learning. The researchers have identified three main categories: Task Independent Recommendation, Configuration Space Design, and Configuration Transfer. By evaluating different datasets, they have found ways to improve how machine learning models make predictions and decisions.