مقایسهٔ روشها
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| بوستینگ× | جنگل تصادفی× | کیسهبندی مقاوم× | |
|---|---|---|---|
| حوزه | یادگیری ماشین | یادگیری ماشین | یادگیری ماشین |
| خانواده | Machine learning | Machine learning | Machine learning |
| سال پیدایش≠ | 1990–1997 | 2001 | 1996–2000s |
| پدیدآور≠ | Schapire, R. E.; Freund, Y. | Breiman, L. | Breiman, L. (bagging); robust variants developed by various authors in 2000s |
| نوع≠ | Sequential ensemble (iterative reweighting) | Ensemble (bagging of decision trees) | Ensemble (robust bootstrap aggregating) |
| منبع بنیادین≠ | 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 ↗ | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| نامهای دیگر | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing |
| مرتبط≠ | 6 | 4 | 6 |
| خلاصه≠ | 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. | 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. | Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions. |
| ScholarGateمجموعهداده ↗ |
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