方法对比
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| 在线决策树× | 决策树× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000 | 1984 |
| 提出者≠ | Domingos, P. & Hulten, G. | Breiman, Friedman, Olshen & Stone |
| 类型≠ | Incremental supervised classifier | Recursive partitioning (if-then rules) |
| 开创性文献≠ | Domingos, P., & Hulten, G. (2000). Mining very fast data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). ACM. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| 别名≠ | Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| 相关≠ | 6 | 5 |
| 摘要≠ | An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time classification on potentially infinite data streams. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. |
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