Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| AdaBoost× | Random Forest× | Pinottava yleistys (Stacking)× | |
|---|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning | Machine learning |
| Syntyvuosi≠ | 1997 | 2001 | 1992 |
| Kehittäjä≠ | Freund, Y. & Schapire, R.E. | Breiman, L. | Wolpert, D.H. |
| Tyyppi≠ | Ensemble (sequential boosting of weak learners) | Ensemble (bagging of decision trees) | Ensemble (heterogeneous meta-learning) |
| Alkuperäislähde≠ | 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 ↗ | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ |
| Rinnakkaisnimet≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| Liittyvät≠ | 5 | 4 | 5 |
| Tiivistelmä≠ | 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. | Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions. |
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