Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| AdaBoost× | Bagging Ensemble× | |
|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Ansambļu mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1997 | 1996 |
| Autors≠ | Freund, Y. & Schapire, R.E. | Leo Breiman |
| Tips≠ | Ensemble (sequential boosting of weak learners) | parallel ensemble |
| 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. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. DOI ↗ |
| Citi nosaukumi≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | bootstrap aggregating |
| 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. | Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models. |
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