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

Authors :
Aldinucci, Tommaso
Lapucci, Matteo
Source :
TOP; Jul2024, Vol. 32 Issue 2, p323-350, 28p
Publication Year :
2024

Abstract

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 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
11345764
Volume :
32
Issue :
2
Database :
Complementary Index
Journal :
TOP
Publication Type :
Academic Journal
Accession number :
178332308
Full Text :
https://doi.org/10.1007/s11750-024-00674-y