Why equations still matter

Predictive accuracy matters. But in an engineering review, classroom or research notebook, the next question is usually: what did the model learn?

Symbolic regression searches for explicit mathematical expressions. Instead of returning only a prediction endpoint, it gives you candidate equations that can be read, plotted, challenged and moved into ordinary code.

Accuracy is only one axis

A complex expression may shave a small amount from test error while making validation and deployment harder. Equarith keeps the trade-off visible with a Pareto front: no single score quietly chooses for you.

Inspection changes the workflow

An explicit equation lets you check units, limits and domain behavior. Residual plots and independent test data still matter—the equation is a model of the observations, not proof of causality—but the review is grounded in something concrete.

Local data, portable result

The search runs on your machine. When you choose a candidate, export it to the language or spreadsheet your project already uses.