Model parameter tuning by cross validation and global optimization: application to the wing weight fitting problem
Model parameter tuning is a fundamental step in any data-fitting problem and of great importance in the final quality of the resulting approximation. Two different sets of model parameters will lead to two different interpolation models that behave very differently between the data points even if both sets of parameters lead to perfect interpolation. The main goal of this paper is to discuss the importance of finding the optimal parameters that will lead to the best prediction model of the given data. This task can be hard, particularly when the number of model parameters is high (usually when the dimension of the problem is high). The wing weight fitting problem is used to illustrate the difficulties in obtaining the best possible approximation in practice.
model parameter tuning cross validation global optimization data fitting principal component regression