Gradient boosted trees and other regression tree models 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. Despite their
advantages, they are generally unpopular for decision-making tasks and
black-box optimization, which is due to their difficult-to-optimize structure
and the lack of a reliable uncertainty measure. ENTMOOT is our new framework
for integrating (already trained) tree models into larger optimization
problems. The contributions of ENTMOOT include: (i) explicitly introducing a
reliable uncertainty measure that is compatible with tree models, (ii) solving
the larger optimization problems that incorporate these uncertainty aware tree
models, (iii) proving that the solutions are globally optimal, i.e. no better
solution exists. In particular, we show how the ENTMOOT approach allows a
simple integration of tree models into decision-making and black-box
optimization, where it proves as a strong competitor to commonly-used
frameworks.