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	Explainable LightGBM	Rozhodovací strom
Odbor	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2017	1984
Tvorca	Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)	Breiman, Friedman, Olshen & Stone
Typ	Gradient boosting with post-hoc explainability (SHAP)	Recursive partitioning (if-then rules)
Pôvodný zdroj	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Ďalšie názvy	XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Príbuzné	6	5
Zhrnutie	Explainable LightGBM combines Microsoft's LightGBM gradient boosting framework with SHAP (SHapley Additive exPlanations) to deliver both high predictive performance and rigorous, theoretically grounded feature-level explanations. It is widely adopted in applied research where predictive accuracy and interpretability are simultaneously required.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.