Methoden vergleichen

	Erklärbares Stacking-Ensemble	Gradient Boosting	Random Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning
Entstehungsjahr	1992 (stacking); 2010s–2020s (explainable extensions)	2001	2001
Urheber	Wolpert, D. H. (stacking); XAI integration developed across the community	Friedman, J. H.	Breiman, L.
Typ	Ensemble meta-learning with post-hoc or intrinsic interpretability	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
Wegweisende Quelle	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	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.
Aliasnamen	XAI-Stacking, interpretable stacking, transparent stacking ensemble, explainable stacked generalisation	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	4	5	4
Zusammenfassung	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.	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.