Sammenlign metoder

	Forklarlig Stacking Ensemble	Bagging Ensemble	Gradient Boosting
Fagområde	Maskinlæring	Ensemblelæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	1992 (stacking); 2010s–2020s (explainable extensions)	1996	2001
Ophavsperson	Wolpert, D. H. (stacking); XAI integration developed across the community	Leo Breiman	Friedman, J. H.
Type	Ensemble meta-learning with post-hoc or intrinsic interpretability	parallel ensemble	Ensemble (sequential boosting of decision trees)
Oprindelig kilde	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Aliasser	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	bootstrap aggregating	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Relaterede	4	4	5
Resumé	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.	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.