Bayesian Symbolic Regression

Understand

Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc.

• Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical expressions composed of some basic functions.
• However, the search space of all possible expressions grows exponentially with the length of the expression, making it infeasible for enumeration.
• Genetic programming (GP) has been traditionally and commonly used in SR to search for the optimal solution, but it suffers from several limitations, e.g.