方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 极端随机树 (Extra Trees)× | Bagging（Bootstrap Aggregating）× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2006 | 1996 |
| 提出者≠ | Geurts, P.; Ernst, D.; Wehenkel, L. | Breiman, L. |
| 类型≠ | Ensemble (extremely randomized decision trees) | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) |
| 开创性文献≠ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| 别名≠ | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. |
| ScholarGate数据集 ↗ |
|
|