Symbolic Regression is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. While conventional regression techniques seek to optimize the parameters for a pre-specified model structure, symbolic regression avoids imposing prior assumptions, and instead infers the model from the data. Common representation of the SR model is an expression tree.
- An example of the expression tree for $x^3+x^2+x$
Genetic Programming (GP) evolves computer programs, traditionally represented as expression tree structures. Some of the applications of GP are curve fitting, data modeling, symbolic regression, feature selection, classification, etc.Read More