Krahasoni metodat

	LightGBM e Shpjegueshme	Pylli i Rastësishëm
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2017	2001
Krijuesi	Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)	Breiman, L.
Lloji	Gradient boosting with post-hoc explainability (SHAP)	Ensemble (bagging of decision trees)
Burimi themelues	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. (2001). Random Forests. Machine Learning, 45, 5–32.
Emërtime të tjera	XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Të lidhura	6	4
Përmbledhja	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.	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.