How to determine over-fitting from non linear least square optimization tool?

I have developed a non-linear equation. It has 4 parameters to be optimized. I have trained the function with 10 experimental data using non-linear least square error optimization method. How can I determine whether my fitting is over-fitting or normal fitting?

Answer 1

[Alan Weiss]

One typical way to do this is by cross-validation, which means fitting a subset of the data and then checking the resulting error against the remaining data for multiple subsets of the data. See, for example, Optimize a Cross-Validated SVM Classifier Using Bayesian Optimization or examples in the crossval function reference page.

Alan Weiss

MATLAB mathematical toolbox documentation