Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

Understand

Symbolic regression searches for analytic expressions that accurately describe studied phenomena.

• The main attraction of this approach is that it returns an interpretable model that can be insightful to users.
• Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms.
• However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization.