So sánh phương pháp

	Rừng ngẫu nhiên	Cây Quyết định	Hồi quy Logistic	Máy Vectơ Hỗ trợ (Phân loại)
Lĩnh vực	Học máy	Học máy	Thống kê nghiên cứu	Học máy
Họ	Machine learning	Machine learning	Process / pipeline	Machine learning
Năm ra đời	2001	1984	1958	1995
Người khởi xướng	Breiman, L.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Cortes, C. & Vapnik, V.
Loại	Ensemble (bagging of decision trees)	Recursive partitioning (if-then rules)	Method	Maximum-margin classifier (kernel method)
Công trình gốc	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Tên gọi khác	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Liên quan	4	5	3	5
Tóm tắt	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.	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.