Compara mètodes

	Apilament d'Empilament Explicable	Bagging Ensemble	XGBoost
Camp	Aprenentatge automàtic	Aprenentatge per conjunts	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1992 (stacking); 2010s–2020s (explainable extensions)	1996	2016
Autor original	Wolpert, D. H. (stacking); XAI integration developed across the community	Leo Breiman	Chen, T. & Guestrin, C.
Tipus	Ensemble meta-learning with post-hoc or intrinsic interpretability	parallel ensemble	Ensemble (gradient-boosted decision trees)
Font seminal	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Àlies	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	bootstrap aggregating	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionats	4	4	5
Resum	Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.