Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Extra Trees× | Bagging (Bootstrap Aggregating)× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2006 | 1996 |
| Autors≠ | Geurts, P.; Ernst, D.; Wehenkel, L. | Breiman, L. |
| Tips≠ | Ensemble (extremely randomized decision trees) | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
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