Symbolic regression

Symbolic regression is a type of regression analysis that searches for mathematical expressions or models that best fit a given set of data. Unlike traditional regression methods, which typically assume a specific form for the underlying function (like linear or polynomial), symbolic regression seeks to discover the structure of the equation itself. Key features of symbolic regression include: 1. **Flexibility**: It does not require a predefined model, allowing it to uncover both simple and complex relationships in the data.