So sánh phương pháp

	Hồi quy Spline Thích nghi Đa biến (MARS)	Cây Quyết định	Mô hình cộng tính tổng quát (GAM)	Gradient Boosting
Lĩnh vực	Học máy	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning	Machine learning
Năm ra đời	1991	1984	1986	2001
Người khởi xướng	Jerome H. Friedman	Breiman, Friedman, Olshen & Stone	Trevor Hastie & Robert Tibshirani	Friedman, J. H.
Loại	Adaptive piecewise-linear regression	Recursive partitioning (if-then rules)	Semi-parametric additive regression model	Ensemble (sequential boosting of decision trees)
Công trình gốc	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Tên gọi khác	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Liên quan	4	5	4	5
Tóm tắt	Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the 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.	A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.