A radial basis function (RBF) is a type of function used in various fields, including machine learning and interpolation. It is characterized by its dependence on the distance from a central point, meaning its output decreases as the distance from this point increases. RBFs are commonly used in algorithms like support vector machines and neural networks to model complex relationships in data. RBFs are particularly useful for tasks such as classification and regression. They can effectively handle non-linear data by transforming it into a higher-dimensional space, allowing for better separation of classes. This makes RBFs a popular choice in applications like pattern recognition and function approximation.