So sánh phương pháp

	K-Nearest Neighbors	Cây Quyết định	Hồi quy Logistic	Naive Bayes
Lĩnh vực	Học máy	Học máy	Thống kê nghiên cứu	Học máy
Họ	Machine learning	Machine learning	Process / pipeline	Machine learning
Năm ra đời	1967	1984	1958	1997
Người khởi xướng	Cover, T.M. & Hart, P.E.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Mitchell, T. M. (textbook treatment)
Loại	Instance-based (non-parametric) learning	Recursive partitioning (if-then rules)	Method	Probabilistic classifier (Bayes' theorem with conditional independence)
Công trình gốc	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.	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.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Tên gọi khác	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Liên quan	5	5	3	4
Tóm tắt	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.	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.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.