Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Bagging (Bootstrap Aggregating)× | Ekstra puud× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 1996 | 2006 |
| Looja≠ | Breiman, L. | Geurts, P.; Ernst, D.; Wehenkel, L. |
| Tüüp≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (extremely randomized decision trees) |
| Algallikas≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| Rööpnimetused≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. | 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. |
| ScholarGateAndmestik ↗ |
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