A Radial Basis Function (RBF) is a type of function used in various fields, including machine learning and statistics, to model complex relationships. 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 for tasks such as classification and regression. RBFs are particularly useful for interpolation and approximation problems, where they help create smooth surfaces through scattered data points. The most common form of RBF is the Gaussian function, which has a bell-shaped curve. By adjusting parameters, RBFs can effectively capture patterns in data, making them valuable tools in data analysis and predictive modeling.