Chi-square as evaluation metrics for nonlinear machine learning regression models
Use your test data to compare the predictive performance of each model.

In R you could do this like:

linear.predictions &lt;- predict(linear.model, newdata = test.data)
nonlinear.predictions &lt;- predict(nonlinear.model, newdata = test.data)

linear.percent.difference &lt;- (test.data$TARGET_VARIABLE -
                              linear.predictions)  /
                              test.data$TARGET_VARIABLE

nonlinear.percent.difference &lt;- (test.$TARGET_VARIABLE -
                                 nonlinear.predictions) /
                                 test.dtat$TARGET_VARIABLE

linear.grade &lt;- mean(linear.percent.difference)
nonlinear.grade &lt;- mean(nonlinear.percent.difference)


This is a pretty simple way to do it, but it is one that works for me and is easy to understand, especially if your audience is going to eye-glaze as soon as you say "Chi-square..." Get creative!