Function approximation is a mathematical technique used to estimate a function that is difficult to compute or unknown. It involves creating a simpler model that closely resembles the behavior of the original function over a specific range of inputs. This is often achieved using methods like polynomial fitting, neural networks, or regression analysis. In many applications, such as machine learning and data analysis, function approximation helps in making predictions or understanding complex relationships within data. By approximating functions, we can efficiently solve problems in fields like statistics, engineering, and computer science, where exact solutions may be impractical or impossible to obtain.