Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| AdaBoost× | Random Forest× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1997 | 2001 |
| Autors≠ | Freund, Y. & Schapire, R.E. | Breiman, L. |
| Tips≠ | Ensemble (sequential boosting of weak learners) | Ensemble (bagging of 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Citi nosaukumi≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateDatu kopa ↗ |
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