  Symbolic regression is a nonlinear regression method which is commonly
performed by an evolutionary computation method such as genetic programming.
Quantification of uncertainty of regression models is important for the
interpretation of models and for decision making. The linear approximation and
so-called likelihood profiles are well-known possibilities for the calculation
of confidence and prediction intervals for nonlinear regression models. These
simple and effective techniques have been completely ignored so far in the
genetic programming literature. In this work we describe the calculation of
likelihood profiles in details and also provide some illustrative examples with
models created with three different symbolic regression algorithms on two
different datasets. The examples highlight the importance of the likelihood
profiles to understand the limitations of symbolic regression models and to
help the user taking an informed post-prediction decision.
