So sánh phương pháp

	Gradient Boosting	CatBoost	Cây Quyết định	LightGBM
Lĩnh vực	Học máy	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning	Machine learning
Năm ra đời	2001	2018	1984	2017
Người khởi xướng	Friedman, J. H.	Prokhorenkova, L. et al. (Yandex)	Breiman, Friedman, Olshen & Stone	Ke, G. et al. (Microsoft)
Loại	Ensemble (sequential boosting of decision trees)	Gradient boosting on decision trees	Recursive partitioning (if-then rules)	Gradient boosting decision tree ensemble
Công trình gốc	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	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.	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.
Tên gọi khác	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
Liên quan	6	5	5	5
Tóm tắt	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	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.	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.