New optimization models for optimal classification trees

Ales, Zacharie; Huré, Valentine; Lambert, Amélie

doi:10.1016/j.cor.2023.106515

Computers & Operations Research

2024

DOI: 10.1016/j.cor.2023.106515

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New optimization models for optimal classification trees

Zacharie Ales,

Valentine Huré,

Amélie Lambert

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2024

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Loss-optimal classification trees: a generalized framework and the logistic case

Aldinucci,

Lapucci

2024

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Classification trees are one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, thanks to remarkable advances in mixed-integer programming (MIP) solvers, several exact formulations of the learning problem have been developed. In this paper, we argue that some of the most relevant ones among these training models can be encapsulated within a general framework, whose instances are shaped by the specification of loss functions and regularizers. Next, we introduce a novel realization of this framework: specifically, we consider the logistic loss, handled in the MIP setting by a piece-wise linear approximation, and couple it with $$\ell _1$$ ℓ 1 -regularization terms. The resulting optimal logistic classification tree model numerically proves to be able to induce trees with enhanced interpretability properties and competitive generalization capabilities, compared to the state-of-the-art MIP-based approaches.

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Loss-optimal classification trees: a generalized framework and the logistic case

Aldinucci,

Lapucci

2024

TOP

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New optimization models for optimal classification trees

Cited by 1 publication

References 35 publications

Loss-optimal classification trees: a generalized framework and the logistic case

Loss-optimal classification trees: a generalized framework and the logistic case

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