方法对比
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| 集成决策树× | 极端随机树 (Extra Trees)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1996–2000 | 2006 |
| 提出者≠ | Breiman, L.; Dietterich, T. G. | Geurts, P.; Ernst, D.; Wehenkel, L. |
| 类型≠ | Ensemble (multiple decision trees combined) | Ensemble (extremely randomized decision trees) |
| 开创性文献≠ | Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| 别名 | decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees) | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| 相关≠ | 6 | 5 |
| 摘要≠ | Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks. | Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time. |
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