So sánh phương pháp

	LightGBM Có Thể Giải Thích	CatBoost	Cây Quyết định	Rừng ngẫu nhiên
Lĩnh vực	Học máy	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning	Machine learning
Năm ra đời	2017	2018	1984	2001
Người khởi xướng	Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)	Prokhorenkova, L. et al. (Yandex)	Breiman, Friedman, Olshen & Stone	Breiman, L.
Loại	Gradient boosting with post-hoc explainability (SHAP)	Gradient boosting on decision trees	Recursive partitioning (if-then rules)	Ensemble (bagging of decision trees)
Công trình gốc	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Tên gọi khác	XAI-LightGBM, LightGBM with SHAP, Interpretable LightGBM, LightGBM explainability	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liên quan	6	5	5	4
Tóm tắt	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.	CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data.	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.	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.