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This document discusses the use of decision trees within the predict-then-optimize framework for decision-making problems involving uncertain input parameters. It introduces a methodology called SPO Trees (SPOTs) that aims to minimize decision error rather than prediction error, providing higher quality decisions with lower model complexity compared to traditional methods. The authors present algorithms for training decision trees directly using the Smart Predict-then-Optimize loss function, addressing the

Author

Jiawen9177

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EN