SPOT: A Toolbox for Interactive and Automatic Tuning in the R Environment

Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. It includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. The suitability of these different meta models for parameter tuning is analyzed in this article. Automated and interactive approaches are compared..