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- W3138703676 abstract "Abstract Classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization and the Mixed-Integer Linear Optimization paradigms to develop novel formulations in this research area. We compare those in terms of the nature of the decision variables and the constraints required, as well as the optimization algorithms proposed. We illustrate how these powerful formulations enhance the flexibility of tree models, being better suited to incorporate desirable properties such as cost-sensitivity, explainability, and fairness, and to deal with complex data, such as functional data." @default.
- W3138703676 created "2021-03-29" @default.
- W3138703676 creator A5025099490 @default.
- W3138703676 creator A5030968370 @default.
- W3138703676 creator A5074052136 @default.
- W3138703676 date "2021-03-17" @default.
- W3138703676 modified "2023-10-16" @default.
- W3138703676 title "Mathematical optimization in classification and regression trees" @default.
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