In "regular" regression, you have to come up with an input -> output equation. The regression only figures out the best parameters for it. This might be diffic

In "regular" regression, you have to come up with an input -> output equation. The regression only figures out the best parameters for it. This might be difficult, especially if you can't visualize the data (many dimensions etc). For example, a numeric regression for a1 * x1 + a2 * x2 + a3 = y can not fit a relationship like y = x1/x2. In symbolic regression, the system can automatically discover equations also, not just coefficients.