方法对比
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| 主动学习决策树× | 决策树× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1984–2010 | 1984 |
| 提出者≠ | Settles, B. (active learning framework); Breiman et al. (decision tree base) | Breiman, Friedman, Olshen & Stone |
| 类型≠ | Active learning with decision tree base learner | Recursive partitioning (if-then rules) |
| 开创性文献≠ | Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| 别名≠ | AL-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| 相关 | 5 | 5 |
| 摘要≠ | Active learning with a decision tree combines the interpretable structure of a CART-style tree with a query strategy that selects the most informative unlabeled instances for human annotation. The model iteratively requests labels only for examples it is most uncertain about, minimising labeling cost while maximising classification accuracy on tabular data. | 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. |
| ScholarGate数据集 ↗ |
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