เปรียบเทียบวิธี

	การถดถอยโลจิสติก	Random Forest	Ridge Regression
สาขาวิชา	สถิติการวิจัย	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Process / pipeline	Machine learning	Machine learning
ปีกำเนิด	1958	2001	1970
ผู้ริเริ่ม	David Roxbee Cox	Breiman, L.	Hoerl, A.E. & Kennard, R.W.
ประเภท	Method	Ensemble (bagging of decision trees)	L2-regularized linear regression
แหล่งต้นตำรับ	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
ชื่อเรียกอื่น	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
ที่เกี่ยวข้อง	3	4	4
สรุป	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.