方法对比
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| 正则化随机森林× | 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≠ | 2012 | 2006 |
| 提出者≠ | Deng, H. & Runger, G. | Geurts, P.; Ernst, D.; Wehenkel, L. |
| 类型≠ | Regularized ensemble (penalized feature selection in trees) | Ensemble (extremely randomized decision trees) |
| 开创性文献≠ | Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| 别名 | RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| 相关 | 5 | 5 |
| 摘要≠ | Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy. | 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. |
| ScholarGate数据集 ↗ |
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