(51f) Data Driven Modeling in Alamo: Feature Selection and Non-Parametric Modeling Applications
When physical interpretability of a model is not prioritized, it is common to use non-parametric regression techniques to provide smooth interpolations of available data. ALAMO is capable of using non-parametric transformations of points in the domain of interest as features for linear model selection. Examples utilizing Gaussian radial basis functions are explored in order to identify optimal interpolative models. Constrained regression techniques used by ALAMO can force these non-parametric models to obey insights enforced by the modeler, enhancing their ability to model accurately in extrapolative domains.
 Hastie, Trevor, Robert Tibshirani, and Ryan J. Tibshirani. "Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso." arXiv preprint arXiv:1707.08692 (2017)