Linganisha mbinu

	Uundaji wa Kuimarisha wa Kuunganisha kwa Nguvu	Bagging (Bootstrap Aggregating)	Kuimarisha	Uimarishaji wa Mteremko	Msitu Nasibu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1992 (stacking); robust variants 2000s–present	1996	1990–1997	2001	2001
Mwanzilishi	Wolpert, D. H. (stacking); robust extensions by multiple authors	Breiman, L.	Schapire, R. E.; Freund, Y.	Friedman, J. H.	Breiman, L.
Aina	Ensemble (stacking with robust meta-learner)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
Chanzo asilia	Wolpert, D. H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Majina mbadala	robust stacking, robust stacked generalization, outlier-resistant stacking, stacking with robust meta-learner	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Zinazohusiana	5	5	6	5	4
Muhtasari	Robust Stacking Ensemble extends classical stacked generalization by replacing the ordinary meta-learner with a robust estimator — such as a Huber-loss regressor, quantile regression, or a model trained on trimmed residuals — so that the ensemble's combination layer is resistant to outliers and noisy base-learner predictions. It improves predictive accuracy and reliability on real-world datasets with contaminated labels or heavy-tailed error distributions.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.