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@unpublished{Thebelt:2020,
author = {Thebelt, A and Kronqvist, J and Mistry, M and Lee, RM and Sudermann-Merx, N and Misener, R},
publisher = {arXiv},
title = {ENTMOOT: A framework for optimization over ensemble tree models},
url = {http://arxiv.org/abs/2003.04774v1},
year = {2020}
}

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TY  - UNPB
AB  - Gradient boosted trees and other regression tree models perform well in awide range of real-world, industrial applications. These tree models (i) offerinsight into important prediction features, (ii) effectively manage sparsedata, and (iii) have excellent prediction capabilities. Despite theiradvantages, they are generally unpopular for decision-making tasks andblack-box optimization, which is due to their difficult-to-optimize structureand the lack of a reliable uncertainty measure. ENTMOOT is our new frameworkfor integrating (already trained) tree models into larger optimizationproblems. The contributions of ENTMOOT include: (i) explicitly introducing areliable uncertainty measure that is compatible with tree models, (ii) solvingthe larger optimization problems that incorporate these uncertainty aware treemodels, (iii) proving that the solutions are globally optimal, i.e. no bettersolution exists. In particular, we show how the ENTMOOT approach allows asimple integration of tree models into decision-making and black-boxoptimization, where it proves as a strong competitor to commonly-usedframeworks.
AU  - Thebelt,A
AU  - Kronqvist,J
AU  - Mistry,M
AU  - Lee,RM
AU  - Sudermann-Merx,N
AU  - Misener,R
PB  - arXiv
PY  - 2020///
TI  - ENTMOOT: A framework for optimization over ensemble tree models
UR  - http://arxiv.org/abs/2003.04774v1
UR  - http://hdl.handle.net/10044/1/77401
ER  -

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