Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pastiprināšana× | Extra Trees× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1990–1997 | 2006 |
| Autors≠ | Schapire, R. E.; Freund, Y. | Geurts, P.; Ernst, D.; Wehenkel, L. |
| Tips≠ | Sequential ensemble (iterative reweighting) | Ensemble (extremely randomized decision trees) |
| Pirmavots≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| Citi nosaukumi | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual 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. |
| ScholarGateDatu kopa ↗ |
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