Machine learningMachine learning
正则化决策树
正则化决策树是一种决策树模型，其复杂性通过剪枝、深度约束或惩罚项有意地加以限制，以防止过拟合。它植根于Breiman等人（1984）的CART框架，正则化将贪婪的树生长过程转化为偏差-方差权衡，从而使模型对未见数据的泛化能力优于完全生长的树。
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Method map
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来源
- Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
- Esposito, F., Malerba, D., & Semeraro, G. (1997). A comparative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5), 476–491. DOI: 10.1109/34.589207 ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Decision Tree (Pruned and Constrained CART). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-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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