Symbolic regression is an interpretable machine learning technique that identifies the specific mathematical equations that best describe a dataset. Unlike traditional regression, which optimizes parameters for a pre-defined model, this approach discovers both the structure and the parameters of a formula simultaneously. While genetic programming has historically been the primary method for evolving these expressions, modern advancements now include neural networks, reinforcement learning, and Bayesian methods. The objective is to achieve a balance between predictive accuracy and mathematical simplicity, often visualized through a Pareto front of potential solutions. Because the resulting formulas are explicit and human-readable, the technique is highly valued for scientific discovery and uncovering natural laws. Significant community efforts, such as SRBench, provide standardized datasets and competition frameworks to evaluate the performance and generalization of various algorithms.
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