So sánh phương pháp

	K-Hàng xóm gần nhất Bayes	Hồi quy Logistic	Rừng ngẫu nhiên
Lĩnh vực	Học máy	Thống kê nghiên cứu	Học máy
Họ	Machine learning	Process / pipeline	Machine learning
Năm ra đời	2002	1958	2001
Người khởi xướng	Holmes, C. C. & Adams, N. M.	David Roxbee Cox	Breiman, L.
Loại	Probabilistic instance-based classifier	Method	Ensemble (bagging of decision trees)
Công trình gốc	Holmes, C. C., & Adams, N. M. (2002). A probabilistic nearest neighbour method for statistical pattern recognition. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 295–306.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Tên gọi khác	Bayesian KNN, BKNN, probabilistic k-nearest neighbors, Bayesian nearest-neighbor classifier	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liên quan	3	3	4
Tóm tắt	Bayesian k-Nearest Neighbors (Bayesian KNN) extends the classical KNN algorithm by placing a prior distribution over the neighborhood size k and combining likelihood evidence from neighbors with that prior to produce calibrated posterior class probabilities. It retains KNN's intuitive instance-based logic while adding principled uncertainty quantification over predictions.	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.	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.