Function Approximation

Function approximation is a method used in mathematics and computer science to find a simpler way to represent complex relationships between inputs and outputs. Instead of perfectly modeling every detail, it creates a close, manageable estimate of how one thing affects another. This is useful when exact models are too complicated or impossible to compute quickly. For example, in machine learning, algorithms learn to predict outcomes by approximating the underlying rules or patterns in data, enabling efficient and accurate predictions in real-world applications.