Machine learningMachine learning
可解释决策树
可解释决策树是一种分类或回归树，其生长过程经过精心设计，使其浅显、易读且可审计——生成一组有限的“如果-那么”规则，人类无需额外工具即可验证。它处于预测建模和可解释人工智能（XAI）的交叉点，适用于利益相关者必须理解并信任模型所做的每一次预测的场景。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8
- Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. DOI: 10.1038/s42256-019-0048-x ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Decision Tree (Interpretable Rule-Based Classification and Regression Tree). ScholarGate. https://scholargate.app/zh/machine-learning/explainable-decision-tree
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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