Global Optimization with Ensemble Machine Learning Models

Abstract Gradient boosted trees and other regression tree models are known to perform well in a wide range of real-world, industrial applications. These tree models (i) offer insight into important prediction features, (ii) effectively manage sparse data, and (iii) have excellent prediction capabilities. We consider holistic decision-making problems where pre-trained tree models are part of larger optimization tasks. Our contributions include: (i) explicitly integrating model uncertainty considerations, (ii) solving the larger optimization problems that incorporate these uncertain tree models, (iii) proving that the resulting solutions are globally optimal, i.e., no better solution exists.