Optimal trees for prediction and prescription

47 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Mathematical optimization in classification and regression trees (2021, methodology): “To reduce this computational burden, a local search approach is proposed in Dunn (2018), where the MILO formulation is solved for the subproblems associated with branch nodes, thus yielding smaller formulations that are solved repeatedly.”
Optimal randomized classification trees (2021, methodology): “tree and manage to get close to improved methods like RFs and the local-search heuristic in [50], respectively.”
Optimal Prescriptive Trees (2019, methodology): “4 of Dunn (2018). We also have seen that setting μ= 0.”
Recent advances in decision trees: an updated survey (2022, methodology): “Both algorithms were subsequently built upon: OCT was extended for regression (Dunn 2018) and prescriptive tasks (Bertsimas et al. 2019), while DTIP was extended for fuzzy trees (Rhuggenaath et al. 2018) and its authors proposed a novel binary formulation called BinOCT (Verwer and Zhang 2019).”