Linganisha mbinu

	Explainable LightGBM	SHAP (SHapley Additive exPlanations)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2017	2017
Mwanzilishi	Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)	Lundberg, S.M. & Lee, S.-I.
Aina	Gradient boosting with post-hoc explainability (SHAP)	Model-explanation method (Shapley-value attribution)
Chanzo asilia	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.
Majina mbadala	XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability
Zinazohusiana	6	5
Muhtasari	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.	SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).