Σύγκριση μεθόδων

	Επεξηγήσιμη Στοίβαξη Συνόλων	Σύνολο Bagging
Πεδίο	Μηχανική Μάθηση	Μάθηση Συνόλων Μοντέλων (Ensemble)
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1992 (stacking); 2010s–2020s (explainable extensions)	1996
Δημιουργός	Wolpert, D. H. (stacking); XAI integration developed across the community	Leo Breiman
Τύπος	Ensemble meta-learning with post-hoc or intrinsic interpretability	parallel ensemble
Θεμελιώδης πηγή	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Εναλλακτικές ονομασίες	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	bootstrap aggregating
Συναφείς	4	4
Σύνοψη	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.